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This chapter is from the book

This chapter is from the book

Historical Analysis

Historical analysis can be categorized into four main areas: time-series visualization, correlation graphs, interactive analysis, and forensic analysis.

Time-Series Visualization

In the preceding section, I mentioned that it is often useful to compare present data with past data. An entire branch of statistics is concerned with time-series analysis, which is the analysis of data that was collected over time. For our purposes, it is not necessary to discuss those statistical principles. This section focuses on how visualization can help analyze time-series data.

What is time-series data? It is simply data that was collected over time. For example, if you are recording all logins to your systems and you not only record the username but also the time the login occurred, you have a time series. There are two goals in time-series analysis:

  • Develop a model to predict future values.
  • Gain an understanding of and analyze the recorded data. What are the variances in time? Does the data show any anomalies? Does the data show any trends? What is the underlying reason or root cause that generated the log entry?

Predictive analysis is always controversial. You have to make sure that you are in a closed system where you are aware of all external factors. Otherwise, external factors suddenly start showing up and may influence the data you are analyzing. This will skew your analysis, and the results will not make sense anymore. In log analysis, the use-cases surrounding predictive analysis are somewhat limited anyway. It is hard to come up with measures that you would want to predict. What good is it to predict the number of failed logins that will occur tomorrow?

To illustrate the influence of external factors in predictive analysis of computer security data, consider the case where you are tying to predict the next attack. You have data about past incidents. Maybe you even conducted a pretty good root-cause analysis and collected log files surrounding the incident. That is nice, but is this enough to actually predict the next attack? Did you really think of all the data points you have to consider, such as the machine's vulnerabilities, the possible attack paths, misconfigurations of the systems, and so forth. How do you take all these factors into account for your predictive model? I am not saying that you could not do it. I am saying that it is hard and you have to be extremely careful when doing so to not forget any of the important factors. I am not going to further discuss this problem, but instead focus on the other part of time-series analysis: understanding the recorded data.

I review five methods to analyze past data: time tables, multiple-graph snapshots, trend lines, moving-average charts, and sector graphs. These methods are not generally used for statistical time-series analysis. Most of the time, some other statistical method is used, but I discovered that these methods lend themselves quite well to analyzing logs files.

Time Tables

One of the graph types introduced in Chapter 3, "Visually Representing Data," was the time table. This type of graph is inherently meant for time-series visualization and lends itself nicely to analyzing and identifying three scenarios:

  • Gaps in activities
  • Periodicity of activities
  • Temporal relationships

An example that shows all three—gaps, periodic behavior, and temporal relationships—is shown in Figure 5-5. The graph plots activity for a set of different target ports. The top series shows port 445 activity. Port 445 is used for all kinds of Microsoft Windows services, such as share access and Active Directory queries. Two clear gaps can be identified in the first half of the graph. Without knowing more about the dataset, it is hard to determine why there are such significant gaps. If this data was collected on a desktop, it could show that the desktop was idle for a while. Maybe the user stepped away for a break. If this was a server that should be under fairly constant load, these gaps might be a bad sign.

Figure 5-5

Figure 5-5 Timetable graph showing periodic behavior, as well as gaps in behavior, and time-related activity.

The next data series shows port 53, DNS-related traffic. This looks like a fairly interesting pattern. First thing to note is the periodicity. Six clusters repeat themselves. Internal to the clusters, there seem to be three groups. Note that the markers are fairly thick, representing more than just one event. What could this be? DNS is an interesting protocol. Normally, a client is configured with multiple DNS servers. If the first server on the list does not respond, the second is contacted; if the second one also fails to answer, the third in the list is being used. Only then will the DNS resolver return an error. Three servers do not always have to be configured. A lot of clients are configured with just one DNS server. The DNS traffic in Figure 5-5 could be representing such a scenario where the first two DNS servers fail to answer. This would also explain the multiple marks for each of the three groups. DNS tries three times for each server before giving up. Assuming this is the right interpretation, this also explains the temporal relationship with the port 80 traffic. After every DNS clusters, there is consistent activity on port 80. This could indicate that the last DNS lookup was successful and thereafter a Web session was initiated.

I could not find an open source visualization tool that would generate a graph similar to the one in Figure 5-5. I therefore used a commercial tool, Advizor, which offers a graph called a timetable.

Multiple-Graph Snapshots

Probably the most straightforward approach to analyzing data over time is to take snapshots at different points in time and then compare them. With some graph types, it is even possible to combine data series of different time frames in a single graph. For example, with line charts, separate lines can be used for different aspects of the data. Figure 5-6 shows an example where each line represents a different server. In this example, there are three servers, and at regular intervals the number of blocked connections to those servers is counted.

Figure 5-6

Figure 5-6 Comparing values over time can be done using multiple data series in a single chart; for example, a line chart can be used to do it, as shown in this figure.

When using multiple charts to compare data over time, make sure you are following these quite obvious principles. Figures 5-7 through 5-10 show, for each principle, how things look if the principle is not followed.

  1. Compare the same data types: apples with apples. For example, do not try to compare different data, such as failed logins last week with successful logins this week (see Figure 5-7).
    Figure 5-7

    Figure 5-7 Compare the same data types: apples with apples. You would not compare failed logins with successful logins.

  2. Compare the same exact values. For example, when monitoring logins, you should keep the same usernames on the graph, even if they have null values. Comparing disjoint sets of usernames is neither efficient nor very useful (see Figure 5-8).
    Figure 5-8

    Figure 5-8 Compare the same exact values. You would not compare across disjoint sets of users.

  3. Compare the values in the same way. Do not sort the charts by the values of the dependent variable, and especially do not do it for one of the charts and not the other(s). Use the same sort of value of the variable for each instance being compared (see Figure 5-9).
    Figure 5-9

    Figure 5-9 Compare the values in the same way. Use the same sorting. You would not sort by the values of the dependent variable in one instance and not the other.

  4. Use the same scale on all the graphs. If one graph uses a scale from 1 to 100 and the other from 1 to 10, a bar filling up 100 percent means completely different things (see Figure 5-10).
    Figure 5-10

    Figure 5-10 Use the same scale on all the graphs.

Figure 5-11 shows an example with three graphs showing user activity at three different points in time. Note how all the four principles are being used. All graphs compare successful logins over the same period of time, a week. They use the same usernames for each of the graphs, although some users failed to log in during specific weeks. The values of the variables appear in the same order, and the scale is kept the same.

Figure 5-11

Figure 5-11 Three snapshots of successful logins at three different points in time. The four principles for point-in-time comparison are being followed.

As you can see in Figure 5-11, all the logins have increased over time, except for aaemisse. This could be a significant sign. On the other hand, this person might have been on vacation.

Figure 5-11 uses a bar chart to compare values over time. Some other charts are fairly well suited for this type of analysis too, whereas others are horrible candidates. Link graphs are probably the graphs least suited for analysis using snapshots over time. The problem with link graphs is that the layout significantly changes even if the underlying data is fairly similar. This results in graphs that look completely different even though the data might be almost the same. Some layout algorithms try to take care of this problem, but I am not aware of any tool that would leverage them.

Treemaps are tricky, too. To make them easy to compare with each other, you need to make sure that the data hierarchy is fairly stable. They are most valuable if the data hierarchy is staying completely stable and just the color is changed. With varying degrees of success, you can also try to change the size of the individual boxes, but it makes comparing multiple treemaps significantly harder.

What about scatter plots? Well, they are actually quite well suited for comparison with each other. The same is true for parallel coordinates. However, for scatter plots it is important to keep the axes the same; and in the case of parallel coordinates, it is important not to overload the graphs. In general, parallel coordinates are better suited for interactive analysis than static snapshots. In some cases, they work really well for static analysis, such as in cases where the dataset is fairly specific.

Trend Lines

Almost in the realm of predicting future values is the determination of a trend line for a data dimension. What is a trend line? A trend line indicates the general direction, or the trend, the data takes over time. Figure 5-12 shows an example with three data series. Each series represents the same data but for different servers. For every day of a week, the number of attacks targeting each server is plotted, along with a trend line for each server. The attack trends for the different servers are all slightly different. Server 3 seems to be in pretty good shape. The attacks are generally in the low numbers, and the trend is decreasing. For the other two servers, it does not look as good. Server 1 shows an even worse trend than server 2. Server 2's trend is rising quickly. However, it is not rising as quickly as the trend for server 1. If I had to prioritize work, I would make sure server 1 is secure!

Figure 5-12

Figure 5-12 A line chart of activity over time. Three datasets are shown. Each refers to a different server that was targeted with attacks. Each of the datasets has its trend line plotted in the graph.

You might be able to make a prediction, also called an extrapolation, of what the data will look in the future, based on a trend line. In Figure 5-12, you would extend the trend line to the right and see where it ends up for future points in time. The values you end up with would quite certainly not be exact. It is likely that the predicted value would not be the same as the actual value. However, it represents a best guess or an educated guess as to what the actual value would be. There is also the possibility that the trend is going to change over time, depending on external factors, such as changes in usage patterns, firewall rules that change, and so on. Essentially, be careful when you are making future predictions. On the other hand, your prediction based off of the trend line is better than a "seat of the pants" prediction (that is, one that is not data based).

Any graph type other than line charts is not well-suited for trend analysis. One of the dimensions needs to be time. The other data dimension can be used for one of two possibilities. The first possibility is any categorical variable in your log: target ports, users, the originating network where a connection came from, or IDS signature name. Count the number of occurrences for a given time period and compare that value over time. The second possibility is to use a continuous variable or data dimension, such as the total number of bytes or packets transferred. Especially for network flow data, these are useful metrics.

A fairly interesting analysis that can be gained from a trend line is a feeling for how anomalous your events are. The distance between each of the data points to their trend is a measure of their anomaly. If you find a point that is very far away (also often referred to as outlier), you have found a significantly anomalous event that might be worth investigating. Be careful with this analysis, however. The data dimension you are investigating needs to have a relationship with time before you can claim any particular data points are anomalies. If your data points appear to be spread randomly, the data dimension under investigation is not likely to have any relationship to time.

You can also make use of a confidence band to summarize the size of the errors or distances between the individual points and their trend, as shown in Figure 5-13. If the value of interest falls within the confidence band, you agree to disregard the deviation from the baseline. If not, you can call it an outlier. This is just a visual tool to aid in detecting anomalous entries.

Figure 5-13

Figure 5-13 A trend line with a confidence band indicates the baseline that is used to plot new values against. If the new values leave the confidence band, they are labeled as anomalous.

Trend Line Graphing Example

Let's walk through a simple example of how to generate a time series graph from iptables log files. I am interested in an analysis of all the blocked outgoing traffic. To do so, I will use a line graph for the last four days of blocked iptables traffic. The graph shows the traffic distributed over 24 hours and does so for each day as an individual data series. The result is shown in Figure 5-14. But let's start at the beginning by looking at an iptables log entry:

May 25 20:24:27 ram-laptop kernel: [ 2060.704000] BLOCK any out: IN= OUT=eth1
SRC= DST= LEN=135 TOS=0x00 PREC=0x00 TTL=64 ID=0 DF
PROTO=UDP SPT=9384 DPT=11302 LEN=115 UID=1000

To generate the desired graph, we need the date and the hour from this entry. All other information we can disregard for now. To extract this information, I use the following command:

sed -e 's/^... \(..\) \(..\):.*/\1,\2/' iptables.log | uniq -c |
awk '{printf("%s,%s\n",$2,$1)}' | sort -r

The output looks something like this:


The first column is the date, the second one the hour of the day, and the third one indicates how many packets the firewall blocked during that hour. To graph this in a line chart, I use following Perl code that utilizes the ChartDirector library to draw the graph in Figure 5-14:

1   #!/usr/bin/perl
2   use perlchartdir;

3   # The labels for the x-axis, which is the hour of the day
4   my $labels = ["0" .. "24"];

5   # reading input
6   my $i=0;
7   while (<>) {
8     chomp;
9     # input needs to have three columns: Day,Hour of Day,Count
10    split/,/;
11    if ($current ne $_[0]) {$current=$_[0]; $i++;}
12    # @data is a day x hour matrix, which contains the count as
13    # the entry.
14    $data[$i-1][$_[1]]=$_[2];
15  }

16  # Generate a line chart and set all the properties
17  my $c = new XYChart(600, 300);
18  $c->setPlotArea(55, 45, 500, 200, 0xffffff, -1, 0xffffff,
      $perlchartdir::Transparent, $perlchartdir::Transparent);
19  # The x-axis labels, which are the hours of the day.
20  $c->xAxis()->setLabels($labels);
21  $c->addLegend(50, 30, 0, "arialbd.ttf", 9)
22  my $layer = $c->addLineLayer2();

23  # Iterate through the days
24  for $i ( 1 .. $#data+1) {
25    $aref = $data[$i];
26    # Making sure no NULL values are present, otherwise
27    # Chartdirector is going to seg-fault
28    for $j ( 0 .. $#{$aref} ) {
29      if (!$data[$i][$j]) {$data[$i][$j]=0};
30    }
31    # Use a grayscale palette to color the graph.
32    my $color = $i * (0x100 / ($#data + 1));
33    $color=($color*0x10000+$color*0x100+$color);
34    # Add a new dataset for each day
35    $layer->addDataSet($aref, $color, "Day ".$i);
36  }

37  # Output the graph
38  $c->makeChart("firewall.png");
Figure 5-14

Figure 5-14 A sample report generated with the firewall.pl script, showing firewall events over 24 hours, split into individual series by day.

To run the script and generate the graph, save it as firewall.pl and execute it with cat out.csv | ./firewall.pl. The output is going to be an image called firewall.png. Make sure you install the ChartDirector libraries before you execute the script. The Perl code itself is not too difficult. It basically takes the CSV input, splits it into multiple columns (line 10), and creates a two-dimensional array (@data), which is later used for graphing. The code on lines 17 to 21 prepares the graph with axis labels and so forth. The final step is to go through each row of the data array, make sure there are no NULL values (lines 28 to 30), and then plot the value as a line in the graph. The color computation (lines 31 to 33) is somewhat fancy. I wanted to use grayscale colors for the graph. The two code lines for the color assignment make sure that each line gets a unique gray tone.

Figure 5-14 shows firewall activity for six consecutive days. The traffic is plotted over a 24-hour period. We can see that for most days, the traffic volume is fairly constant over the day. However, day 1 shows a completely different pattern. It shows spikes at 8 in the morning, at 2 p.m., at 4 p.m., and between 7 p.m. and 8 p.m. This seems a bit strange. Why would there be no traffic for certain times, and why are there huge spikes? Was there maybe some kind of infrastructure outage that would explain this phenomenon? This is worth investigating, especially because all the other days show regular behavior.

If you are interested in pursing these ideas some more with statistical methods, have a look at the next section, where I discus moving averages.

Moving-Average Charts

Trend lines are only one way to look at how your time-series data is evolving over time. Another method that is commonly used in stock price analysis is a moving-average analysis.2 A moving average helps to smooth data values. Smoothing data values has the effect that individual outliers show up as less extreme. They are adjusted in terms of the rest of the data. It therefore makes it easier to spot trends. This is especially useful for volatile measures (that is, measures that change a lot over time).

How (and more important, why) would you look at moving averages? They are useful for analyzing trends of various measures and are an alternative to trend lines. Moving averages are more precise, and the analysis methods I show you here can prove useful in decision making based on time-series data.

As a decision maker, you need to know when exactly to make a decision based on a set of measures. You can try to look at a trend line, but the trend line is too generic. It does not react well to change. If you are the holder of a certain stock, you want to know when to sell. When monitoring attacks targeting your network, by either looking at firewall logs or intrusion detection logs, you need to know when the number of attacks starts deviating too much from the normal amount so that you know to start investigating and addressing a potential problem. I show you ways to make that decision.

Simple Moving Average

A moving average is computed by taking the average of data values over the last n values, where n defines the period for the moving average. For example, a 5-day moving average is computed by adding the values for the past 5 days and then dividing the total by 5. This is repeated for every data value. This process smoothes individual outliers and shows a trend in the data. The result of this procedure is illustrated by analyzing the risk associated with unpatched systems in a large network (see sidebar). A graph showing the risk of unpatched machines is shown in Figure 5-15. The figure shows the actual data values along with their moving average. You can see that moving averages are lagging indicators. They are always "behind" the actual data values.

Figure 5-15

Figure 5-15 Example of applying a simple moving average to see the risk associated with unpatched machines. The three crossover points indicate a change in trend development.

By defining a high and a low threshold, we can determine when an activity has to be triggered. You can see in Figure 5-15 that for the moving average the spikes are smoothed and thresholds are less likely to be crossed as compared to the raw data, unless there is a real trend in the data. Crossover points of the data line and the moving average line mark a potential decision point. When the moving average crosses the data line, the data is significantly moving against the moving average, and therefore breaking away from the norm.

Advanced Moving Averages

The issue with moving averages is that the lag is significant. Various methods help address this problem. For example, exponential moving averages3 (EMA) are used to reduce lag by applying more weight to recent values relative to older values. The result of calculating an EMA on the data is shown in Figure 5-16.

Figure 5-16

Figure 5-16 Example of an exponential moving average, compared to its simple moving average.

Instead of EMAs, which address the problem of lag, we can also use a dual moving average analysis, where two moving averages of different time periods are used. The crossover points indicate upward trends when the shorter period moving average moves above the longer term moving average; it indicates a downward trend otherwise.

You might realize that this type of comparison is still fairly poor for the given data. There are three decision points. The second and third one seem to be not well placed. Just because there was one value that was higher on May 10 does not necessarily mean that things have changed for good. We need a better method than the simple moving average to reduce the number of decision points.

A more sophisticated analysis can be done by using a moving-average convergence/divergence analysis (MACD). It addresses some of the shortcomings of the simplistic methods I introduced earlier. It takes two moving averages, one over a longer period of time and one over a shorter period of time, and computes a measure based on the difference of the two. I borrowed this analysis from stock analysts.4 A sample MACD chart is shown in The challenge is to come up with a good time period for the two moving averages. The period depends on your use-case and your data. The shorter the period, the quicker you are prompted to make a decision. The longer the period, the less reactive the analysis is to local spikes. To give current values more weight than older ones, an EMA can be used for both of the moving averages.

Figure 5-17 was generated with Excel, by calculating the individual values for each of the EMAs and then plotting them in the graph. I do not discuss the MACD analysis and the chart in Figure 5-17 any further. Some people say that the analysis is pure voodoo. It is difficult to define the right time periods for the EMAs. Changing them can significantly change the location of the decision points. Each application has its own optimal values that need to be determined.

Figure 5-17

Figure 5-17 MACD chart with a 5- and 10-day EMA. The thick black line is the MACD line, computed by subtracting the 5-day EMA from the 10-day EMA. The 3-day EMA is plotted as the gray line, and the histogram at the bottom shows the difference between the MACD and the 5-day EMA. The histogram is positive when MACD is above its 3-day EMA and negative when MACD is below its 3-day EMA. A signal is generated when MACD signal crosses the MACD.

Applying Moving Averages

Here are some rough guidelines for when to use moving average analysis and when not to:

  • Use moving average analysis to get information about trends and changes in trends.
  • If you deal with data values that do not show a general trend—the data seems fairly chaotic—moving-average analysis is not very well suited.
  • The moving-average time period is a parameter that needs to be carefully chosen. The smaller the time period, the more the moving average becomes reactionary. This means that you are reacting to the data more quickly and you are dealing with the problem of "false positives."
  • Do not use this type of analysis for measures that can uniquely be associated with "good" or "bad." For example, a risk of 10 is "bad," and a risk of 1 is "good." You do not need moving averages to tell you when to react to the risk development. You can define a risk of 6 or higher as noteworthy.

    Or positively formulated:

  • Use moving-average analysis for measures that do not have set boundaries. For example, the number of packets blocked by your firewall is a measure that has no boundaries. You do not know what a good or a bad value is. However, you want to know about significant changes.

Often, it is useful and necessary to capture the trend behavior at the present point in time. Whereas moving average charts show you the development of your metric over a longer period of time, you can use sector graphs to quickly capture the trend at a certain point in time, generally in the present.

Sector Graphs

An interesting way of analyzing the current state of a time-series dataset is by using a sector graph. The New York Times uses this type of graph to show the performance of stocks or markets.5 The idea of the chart is simple. You take a time-series and fix a point in time that you want to analyze. Calculate the percentage change of the value from the point in time you chose to the value a day ago. Then do the same for the value you chose and its value a week ago. Assume you get a 5 percent change since the day before and a –10 percent change compared to a week ago. These two values now define a point in a coordinate system. Plot the point there. Repeat this for all the time series that you are interested in. By looking at the sector graph, you will get a comparison between all the series.

Instead of choosing a day and a week as the time periods, you can take any other time period that you are interested in. What is of importance is where a point for a time series is going to land in the coordinate system. If the point lands in the upper-right quadrant, for example, you are dealing with a series that has performed well over both periods of time. If the point lies in the bottom-right quadrant, you are dealing with a series that has done well over the short period, but not so good over the long period. It's an improving series. Analogous statements can be made for the lagging and slipping quadrants.

Instead of just drawing simple data points in the quadrants, you can use color and size to encode additional information about the time series. Color, for example, can be used to distinguish between the different series.

An example of how to use a sector chart is given in Chapter 7, "Compliance." There the chart is used to show the development of risk in different departments.

Figure 5-18 shows an example of a sector chart that was generated with Microsoft Excel. The exact steps of generating this graph can be found in Chapter 7. Figure 5-18 shows two data points. The values encode the number of incidents recorded for the Finance and the Engineering departments. The current number of incidents in the Engineering department is 25. The data point is located on the top right, which means that the incidents in the Engineering department have constantly been rising. This is a concern. The finance department shows a current number of 14 incidents. The data point lies in the bottom-left quadrant, which indicates that a constant decrease of incidents has been recorded. This is a good sign.

Figure 5-18

Figure 5-18 Sector chart with explanations of what it means when data points are drawn in the specific quadrants.

Correlation Graphs

Correlation graphs can be used, like time-series analysis, to analyze data by assessing the extent to which two continuous data dimensions are related. In other words, you want to know for values in one data dimension, do the values of the other data dimension correspond in some orderly fashion? There are two ways to use a correlation graph to analyze log data. Either two data dimensions of the same log file are correlated with each other or the same data dimensions are correlated for different log files. A correlation graph of two data dimensions of the same log entry is used to show how one dimension is related to another. For security log files, this is not very interesting. Different fields from the same log file are not correlated, unless it is already inherently obvious. For example, the event name and the target port are generally correlated. The target port determines the service that is accessed and therefore dictates the set of functionalities it offers. This set of functionalities is then generally expressed in the event name.

Correlation in this context works only with continuous or ordinal data. This already eliminates a lot of data dimensions such as IP addresses and port numbers. Although they could be considered continuous data, in most of the cases they should be treated as values of a nominal variable. There is no inherent ordering that would say that port 1521 is worth more or more important than port 80. It is just a coincidence that Oracle runs on a port that is higher than 80. So what are the data fields that make sense for correlation graphs? Well, they are very limited: Asset criticality is one, for example. This is generally an additional data field that is not contained in the log files, but here you can clearly make a statement about an order. What are some other fields? Traffic volumes, such as bytes or packets transferred, event severities or priorities, and the file size are all continuous variables. That is pretty much it. Unfortunately. This also means that there is not much reason to actually use correlation graphs in this simple form for log correlation.

If we are expanding our narrow view a little bit and we shift our focus away from only log entries and their data dimensions, there are some interesting places where correlation graphs can be applied. What if we try to take aggregate information—for example, the total number of vulnerabilities found on a system during the past day—and correlate that number with the amount of money invested into each system for vulnerability remediation? These are not individual log entries anymore, but aggregated numbers for vulnerabilities and cost. Suddenly, correlation graphs are an interesting tool. Is the number of vulnerabilities directly correlated with the money we spend on vulnerability management? We hope it is negatively correlated, meaning that the number of vulnerabilities goes down if the money invested in vulnerability management is increased.

A somewhat more complex example is shown in Figure 5-19, where a correlation matrix is drawn. I took four data dimensions that were measured in regular intervals. The matrix shows the correlations between each of the four data dimension. The figure shows the individual correlation graphs, where the data points from two dimensions are presented in a scatter plot. Each of the graphs also contains their trend line and the correlation coefficient of the two dimensions. When looking at the trend line, you have to manually inspect the pattern of the data points. Do they run from the bottom-left corner to the top-right corner as in a positive correlation? Do they run from the top-left corner down toward the right-bottom corner as in a negative correlation? How close is each data point to the trend line itself? The closer to the line, the stronger the correlation. If they are randomly dispersed all over the place and do not group around the line, there is no correlation. This is the case in all the graphs in the first column of Figure 5-19. This means that the number of incidents is not related to any of the other data dimensions: Employee Hours, Personnel, or Cost. On the other hand, these three data dimensions are somewhat correlated with Employee Hours and Personnel, showing a strong correlation.

Figure 5-19

Figure 5-19 Correlation matrix showing the relationships among multiple data dimensions. It shows how the security investment (Cost), hours spent on security-related projects (Employee Hours), and the number of incidents, as well as personnel needed to clean up an incident, are related to each other. The number indicates the strength of the correlation between the two data fields.

The correlation coefficients shown in each of the graphs are mathematical indices expressing the extent to which two data dimensions are linearly related.6 The closer to 1 (or –1) the correlation coefficient is, the stronger the relationship.

How do we read the correlation matrix in Figure 5-19? You can clearly see a trend in the data points for Employee Hours and Personnel. They group nicely around the trend line. To a lesser extent this is true for Employee Hours and Cost and Personnel and Cost. Second, have a look at the correlation coefficients. They make a statement about whether the data dimensions are linearly related. Again, you will find that our two data dimensions of Employee Hours and Personnel are showing a fairly high value, which means that they are strongly linearly related. If one increases, the other will, too. That just makes sense; the more personnel who respond to an incident, the more hours will be burned. It seems interesting that the cost is not more strongly related to the employee hours. There must be some other factor that heavily influences cost. It could be something like there is considerable variability in the payscale of the personnel. It also seems interesting that the number of incidents is not related to any of the other data dimensions. I would have expected that the more incidents, the more expensive it would be to address them—but perhaps once personnel are called to respond to one incident they stay around and address further incidents. It would take some investigating to nail down these other influences on the data.

Interactive Analysis

So far, we have used static images or graphs to represent data. Once the input data was prepared, we defined the graph properties, such as color, shape, and size, and used it to generate the graph. During the definition process, we generally do not know how the graph will turn out. Is the color selection really the optimal one for the data at hand? Is there a better data dimension to represent size? Could we focus the graph on a smaller dataset to better represent the interesting parts of our data? What we are missing is a feedback loop that gives us the possibility to interactively change the graphs instead of backtracking to make different choices.

In the Introduction to this book, I mentioned the information seeking mantra: Overview first, zoom and filter, then details on-demand. I am going to extend this mantra to include an additional step:

  1. Overview first.
  2. Change graph attributes.
  3. Zoom and filter.
  4. Then details on-demand.

The second and third steps can be repeated in any order. Why the additional step? You could choose the graph properties before generating the first graph. However, this is one of the disadvantages of static graphs. You do not generally know how the graph will look before you have generated a first example. Looking at a first instance of a graph significantly helps to make a choice for the other graph attributes. It is also useful to change the graph attributes, such as color, on demand to highlight different portions of the data. After some of the attributes have been adapted and a better understanding of the data has been developed, a zoom and filter operation becomes much easier and effective.

The second and third steps of the new information seeking mantra are called dynamic query in the visualization world. A dynamic query continuously updates the data filtered from the database and visualizes it. It works instantly within a few milliseconds as users adjust sliders or select buttons to form simple queries or to find patterns or exceptions. Dynamic queries have some interesting properties:

  • Show data context: How do data entries look that are similar to the result, but do not satisfy the query? Conventional queries only show the exact result, whereas dynamic queries can also display data that is similar to the result. This is often a useful thing to know to understand the data better.
  • Dynamic exploration: Investigations, such as "what if" analysis, are intuitively possible.
  • Interactive exploration: User-interface support, such as sliders, can be used to change the value of a variable interactively.
  • Attribute exploration: The data of a single data dimension can be analyzed and explored interactively.

These aspects are all covered by dynamic queries. Keep in mind dynamic queries are a type of user interface. Behind the scenes, systems that support dynamic queries need a way to query the underlying data stores. This is often done through conventional query languages such as SQL.

Dynamic queries are unfortunately not supported by many tools. Most of the ones that exist are in the commercial space. Second, if you have used one of those tools, you know that the amount of data you can explore is fairly limited and is generally a factor of the amount of memory you have available. To support efficient dynamic queries, those tools need to load all the data into memory. Make sure that for large amounts of data you limit the scope of individual queries and work on a sample before you expand your view to the entire dataset.

A second interface concept that supports data exploration is the use of linked views. Each type of graph has its strengths when it comes to communicating data properties. You read about these properties in Chapter 3, "Visually Representing Data." To explore data, it is often useful to apply multiple different graphs to see various properties simultaneously. Using a display composed of multiple types of graphs can satisfy this need. To make this view even more useful, it should enable user interaction (i.e., support dynamic queries). The individual graphs need to be linked, such that a selection in one graph propagates to the other ones. This is an incredibly powerful tool for interactive data analysis.

The different types of graphs support different analysis use-cases. Bar charts, for example, are suited for attribute exploration. They are good filtering tools, too. Attribute exploration is a method used to analyze a single data dimension. What are the values the dimension assumes? How are the values distributed? Do some values show up more than others? Are there outliers and clusters of values? All these questions can be answered with a simple bar chart showing the frequency of each of the data values. Figure 5-20 shows an example with two linked bar charts, illustrating the concepts of attribute exploration and linked views. The bar chart on the left shows the count of users in the log file. Most activity was executed by the privoxy user. The rightmost side shows the ports used. Only two ports show up, www and https, indicating that we are dealing with Web connections. As the bar chart on the right shows, only about an eighth of connections were secured, meaning that they used HTTPS. On the rightmost bar chart, the secure connections (https) are selected. This selection propagates to the linked bar chart on the left side. We can now see that most secure connections were executed by the privoxy user, followed by ram and debian-tor. Root executed no secure connections at all. Why? Is this a problem? This simple example shows how a bar chart can be used for attribute exploration to show both secure and insecure connections by user.

Figure 5-20

Figure 5-20 Linked bar charts, illustrating the concepts of attribute exploration and linked views. The left side shows the number of log records that contained each of the users. The right side shows the protocol associated with the users' activities. The https protocol bar on the right side is selected, and the selection is propagated to the linked bar chart on the left side, showing that most of the HTTPS connections were executed by privoxy, some by ram, and the rest by debian-tor.

What are some other charts and their role in interactive analysis and linked views? Scatter plots, for example, are a good tool to detect clusters of behavior. We have discussed this in depth already. Using scatter plots simultaneously with other linked views has a few distinct advantages. Interactivity adds the capability to detect clusters interactively and explore the data by selecting the values. This immediately reflects in the other graphs and shows what the clusters consist of. The other chart types, such as line charts, pie charts, parallel coordinates, and so on, can all be used in a similar fashion to represent data. I have already discussed the strengths and applications of all of these charts. All the benefits outlined for scatter plots apply to the other types of graphs, too. Linked views significantly improve the data-exploration process.

Not only selections can be propagated among graphs, but also the choice of color. Each of the graphs can use the same color encoding. This is yet another way that linked views are useful. Instead of using a separate graph to analyze a specific data dimension, that dimension can be used to define the color for the other graphs.

A great tool for interactive data analysis is the freely available ggobi. A detailed discussion of the tool appears in Chapter 9. Figure 5-21 shows a screen shot of ggobi, giving you an impression of how an interactive analysis looks. Note how the different graph types are used to highlight specific data properties. In addition to the graphs themselves, color is used to encode an additional data dimension. It immediately communicates the values of the data dimension and shows how this dimension is related to all the others.

Figure 5-21

Figure 5-21 An interactive analysis executed with ggobi. Multiple views of the same data simplify the analysis by showing multiple data dimensions at the same time. Color is used to highlight specific entries, and brushing can be used to interact with the data.

The screen in Figure 5-21 is divided into two parts. The left side shows settings for the graphs and a window into the underlying data—the data viewer. The bottom part shows the configuration of the color bins. By moving the sliders, you can interactively change the color assignments for the three data variables that are visualized. The right side of the screen shows three different views into the data. By analyzing all three views, we get a feeling for all the data dimensions. We can see which machines are using which ports and which users are associated with that traffic through the parallel coordinates. We can identify how the data dimensions are correlated, if at all, by using the scatter plot matrix, and the bar chart can be used to see the distribution of the IP address values. Selecting data values in one graph propagates the selection through the other graphs. This supports the interactive analysis of the data.

Forensic Analysis

All the concepts discussed in this chapter to this point were methods of analyzing data. How do we put all of these methods and concepts to work to tackle a real problem: the forensic analysis of a dataset unknown to the analyst? Forensic analysis can be split into three use-cases:

  • Data exploration to find attacks, without knowing whether attacks are present
  • Data exploration to uncover the extent and exact path of an attack
  • Documentation of an incident

The second and third use-cases should be integral parts of the incident response (IR) process. Visualization not only helps speed up and facilitate the process of analyzing data, it is also a powerful tool for documenting an incident. I am not going into detail about how your IR process can be enhanced to use visual tools because IR processes differ slightly from company to company. However, given an understanding of the last two use-cases, it is a natural extension to include visualization in your own IR process.

Finding Attacks

Trying to uncover attacks through log analysis is not an easy task. This is especially true if there are no hints or you have no particular reason to be suspicious. It is much like trying to find the proverbial needle in a haystack. Visualization should play a key role in this detection process. We can learn from the visualization world about how to approach the problem. We have come across the information seeking mantra a couple of times already. We will see that it has its place in forensic log analysis, too. The first analysis step according to the information seeking mantra is to gain an overview. Before we can do anything with a log file, we have to understand the big picture. If the log is from a network that we already know, it is much easier, and we can probably skip the overview step of the analysis process. However, the more information we have about a log and its data, the better. We should try to find information about the contents of the log file from wherever we can. Information such as that which can be gathered from people who operate the networks we are about to analyze or system administrators responsible for the machines whose logs we have can help provide needed context. They can all help us interpret the logs much more easily and help us understand some of the oddities that we will run into during the analysis process.

All the principles discussed earlier around interactive analysis are useful to achieving an efficiently conducted forensic log analysis. Many questions about the log files can be easily answered with dynamic queries. For example, what services is machine A using? Nothing easier than that. Generate a linked view with two bar charts. The first bar chart shows the source addresses, and the second one the target ports. Select machine A in the first chart and have a look at the linked selection in the target port bar chart. Find something interesting? Follow it and explore the data, one click after the other.

Unfortunately, there is no simple recipe for forensic log analysis that is independent of the type of log file to analyze. Each type of log file requires specific analysis steps. For certain types of logs, however, there are commonalities in the analysis process. I will introduce an analysis process that tries to exploit these commonalities.

The complete process for forensic log analysis is shown in Figure 5-22.

Figure 5-22

Figure 5-22 Forensic analysis process summary diagram. Ovals represent data sources, and the boxes contain the individual analysis processes.

The analysis process differs significantly depending on the type of log file that is analyzed. If it is a network-based log—anything from packet captures to network-based IDS logs—certain analysis steps apply. If a host-based log has to be analyzed, either on the operating system level or even on the application level, different analysis steps are necessary.

For the discussion of the forensic analysis process, I break the process up into different phases based on the diagram in Figure 5-22, and will therefore start with network flow data.

Network Flow Data

Network flow records are a great tool for gaining an overview of a forensic situation. Keep in mind that network flow data can be derived from other log types, such as packet captures, and in some cases firewall log files, and sometimes even from NIDS logs. I discussed this in depth in Chapter 2, "Data Sources." We first need to gain an initial understanding of the network traffic that we are analyzing. Here are the graphs we will generate:

  1. Top Talkers (source and destination)
  2. Top Services
  3. Communication Graph

We start by generating some overview graphs to see the hosts and their roles in the network. Use a bar chart to show the frequency of connections seen for both source and destination addresses. Sort the charts by the connection frequencies to see the top "talkers" on the network. If you have data about machine's roles, use it as color in the chart. Make sure you are focusing on the most important roles so as to not overload the charts with color. Do the same for the destination ports. Use the machine's role as the colors again.

So far, the graphs do not reveal relationships between machines. You could use an interactive tool to explore the relationships by selecting different machines in the bar charts and simultaneously monitoring the change in the other charts, but there is a better solution. Use a link graph that displays the source and destination addresses in a communication graph.

You could use the amount of traffic to encode the thickness of the edges between the machines. You can measure the amount of traffic in either bytes, packets, or as number of passed packets, and so on. There are no limits to your creativity. If you use firewall logs to begin with, color the edges based on whether traffic was blocked or passed. If packets are passed and blocked, use yet another color.

Figure 5-23 shows all four graphs for a sample firewall log. I decided to show only the top 15 sources and destinations. Otherwise, the bar charts would be illegible. The same was done for the services. To match the data in the bar charts, the link graph shows only traffic between the top 15 addresses, too. The link graph colors machines that are managed by us in light gray. All other machines are dark gray. Also note that the edges (i.e., arrows) are colored based on whether the traffic was blocked. Dark edges indicate blocked traffic.

Figure 5-23

Figure 5-23 Gain an overview of the traffic in the log file. Who are the top talkers, what are the services accessed, and what is the relationship between these machines?

This gives us a first overview of what the log file is about. To explore in further detail how the graphs were generated, check the sidebar.

Using the graphs we generated, we should now try to figure out whether we have found any visible anomalies. Questions we can try to answer include the following:

  • Is there a source or destination host that sticks out? Is there a host that generates the majority of traffic? Why is that? Is it a gateway that possibly even does Network Address Translation (NAT)? This would explain why it shows up so much.
  • Is a certain service used a lot? Is that expected? If TFTP is the service used the most, for example, something is probably wrong.
  • Are there services that were not expected? Is Telnet running on some systems instead of SSH?
  • Are some machines communicating with each other that should not be?

Fairly quickly, after answering all these questions, you will want to know which services each machine is offering. To best visualize this information, use a treemap, as shown in Figure 5-24. Treemaps are well suited to encode a lot of information in a small area. They allow us to easily analyze the distribution of protocols in the network traffic.

Figure 5-24

Figure 5-24 Treemap showing the machines on the network along with the services they are being accessed on.

We can easily see that there is one machine, .42, that gets most of the traffic on port 80. The other machines are so small that they seem to be hidden. Therefore, I generated a second graph of the same traffic, this time filtering out machine .42. Figure 5-25 shows the result. Now we can see all the machines and the ports they were accessed on. In the case of visualizing a firewall log, we could use different colors to reflect whether the traffic was blocked or passed.

Figure 5-25

Figure 5-25 Treemap showing machines and their services. This time the dominant machine is filtered to show all other machines.

Figure 5-25 shows that a lot of machines were targeted with FTP. The next step is to verify whether these connections were indeed successful, and if so, whether those machines were meant to have FTP running? You can verify this by either looking at the raw packet captures or executing a port scan of those machines.

From here, the next step in analyzing network flow data is to define some hypothesis about the data and then verify the data against them. By thinking about your environment and what types of activities you could encounter, you can come up with a set of assumptions about what traffic might be interesting to look at. The hypothesis does not necessarily have to be true. By applying the hypothesis to the log file, it will help confirm whether the hypothesis was right or wrong. Consider this sample hypothesis: You are trying to uncover worm attacks by saying that a worm-infected machine will contact a lot of other machines and generate extensive communication patterns that will be readily visible in communication graphs.

Various other graphs can be used to analyze network flow data in more detail and find possible attacks. The generic analysis steps did not necessarily uncover attacks. Most of the times it is the application of a hypothesis that will help uncover anomalies and, possibly, attacks. In Chapter 6, "Perimeter Threat," I present some hypotheses that help, for example, uncover DoS attacks or worms. To be honest, detecting these two cases is not rocket science because the volume of traffic involved in both of these attacks is quite significant. Again, other use-cases depend specifically on the data and use-cases that are of interest. Here I gave an introduction and a starting point for your quest. You can find more use-cases involving network flows in Chapter 6, where I show an example of how to monitor network usage policies to uncover unwanted behavior.

Table 5-1 shows the individual analysis steps that you can use to analyze network flow data. If you collected packet captures and not just network flows, you can use an additional step to analyze your data. Run your packet captures through an intrusion detection system to see whether it finds any attacks. To do so with Snort, run the following command:

snort -l /var/log/snort -c /etc/snort.conf -U -A full -r <pcap_file>

Table 5-1. Summary of Network Flow Data Analysis Steps



1. Gain an overview.


  • Top talkers
  • Services overview
  • Machine relationships

2. Analyze overview graphs.

Can you find any anomalies in the previous graphs? Verify the top talkers, services, relationships, and so on.

3. What services are target machines offering?

Generate a treemap that shows the services per machine.

4. Verify services running on machines.

Are there any machines that should not be offering certain services? Analyze the previous graph, keeping your network configuration in mind. A DNS server should probably not expose a Web server, either.

5. Hypothesis-based analysis.

Come up with various hypotheses for analyzing your network flows.

This command writes the Snort log file into /var/log/snort/alert. The additional IDS events generally uncover a significant amount of additional data. The next section shows how to deal with all this information.

Intrusion Detection Data

What can we do with network-based intrusion detection data? The difference from the data we have discussed before is that NIDS data shows only a subset of all connections, namely those that violated some policy or triggered a signature. The only thing we can do to figure out which machines are present on the network and what their roles are is to treat the limited information in the IDS logs as network flow data. This is definitely not a complete picture, but it at least shows the machines that triggered IDS alerts in relationship to each other. However, we can do more interesting and important things with IDS logs.

IDS logs can, to a certain degree, be leveraged to prioritize and assess machines and connections. How hard has a target machine been hit? How "bad" is a source machine? How malicious is a connection? To do so, we need to define a prioritization schema. The higher the priority, the worse the event. I am assuming a scale of 0 to 10, where 10 is a highly critical event. As a starting point for calculating the priority of an event, we are using the priority assigned by the IDS, sometimes called the severity. We might have to normalize the numbers to be in the range from 0 to 10, which in some cases requires the conversion from categorical values, such as High, Medium, and Low to numeric values. Unfortunately there is no standard among IDSs to use the same ranges for assigning a priority to events. Some use scales from 0 to 100, others use categorical values. Based on this initial score, four external factors are applied to skew the score:

  • The criticality of the target machine: This requires that every target machine is classified based on its criticality to the business. Machines that contain company confidential information should be rated higher than test machines.
  • History of the sources: Keep a list of machines that were seen attacking or scanning your network. Machines that have scanned your network before will get a higher score. Ones that have attacked machines on your network will also get a higher score.
  • Chance of success: Is the port indicated in the event open on the target machine? Often, IDSs report attacks that did not have a chance to succeed (for example, because the target port was not open). If the target port was not open, lower the priority score.
  • Vulnerability status: Is the vulnerability that the attack was trying to exploit exposed and present on the target machine? If it was not present, lower the priority score.

To validate the last two points, you might need a vulnerability scan of the target machines. If those two conditions are false, you can drastically decrease the priority of your event. The attempted attack does not have the potential to significantly harm you. This type of event prioritization is something that security information management (SIM) solutions are using to calculate priorities for events and help get everyone focused on the important ones. You can also use other factors to rate an event. Depending on your environment, you might want to consider doing so.

Now that you have a priority for each event, you can first assign this to each event and then plot them atop your already existing communication graphs. The figure shows only priority 9 and 10 events so as to not overload the graph. In addition, for the priority 9 events, all the external nodes, machines that are not situated on our network, are aggregated to a single "External" node. For the priority 10 events, I show the exact address for the node. These configuration decisions are simply measures taken to prevent overloading of the graph and to keep it legible.

The edges (i.e., arrows) are colored based on the priority. The darker the edge, the higher the priority. If a link was seen multiple times, the maximum of the individual priorities was used to choose a color for the edge.

If you are fortunate enough to have NIDS logs and network flows, you can execute numerous interesting analysis tasks. The following discussion assumes that you have both types of logs available. Some of the analysis also works if you have only NIDS logs.

What are the next steps to take after we have a graph that helps prioritize individual connections, such as the one in Figure 5-26? We should look at the graph and analyze it. What are the things we can identify that might hint at a problem or a potential attack? There are many things to look for, and some of them might require us to generate some additional graphs. For now, let's concentrate on the graph we have (Figure 5-26). We should start by defining some hypotheses about attacks that we can then look for in the graph. What are some of the things we would expect to see, and what are the questions we need to ask if there was indeed an attack hidden in the logs?

  1. Based on the scoring of nodes and the exclusion of all low-priority connections, what are serious and important events? This is an important question to keep in mind. At this point, we should think hard before we dismiss a specific communication.
  2. Do clusters of nodes behave in similar ways? Why is that? Do the targets in that cluster have any properties in common? Are there outliers in the cluster? Why?
  3. If a machine gets successfully compromised, it might start to initiate sessions back to the attacker. Are there any such instances? This is where traffic flows can help provide the information about new sessions.
  4. Do machines try to initiate sessions that never get established? This is common for scanning activity where ports are probed to see whether a service will answer back.
  5. Do any connections show a strange combination of TCP flags? Any connection that does not comply with the RFCs, thus violating protocol specifications, is a candidate.
  6. Do any connections have an anomalous byte count? This analysis should be done on a per protocol level. DNS over UDP, for example, should always have a certain packet size. HTTP requests are normally smaller than the replies. And so on.
Figure 5-26

Figure 5-26 A network communication graph generated from network flow data. In addition to the network flows, IDS data is used to color the edges with the corresponding priorities.

This list is by no means complete. Many other hypotheses could be established and checked for. For now, the link graph in Figure 5-27 shows the same graph as in Figure 5-26, but this time it is annotated with all hypotheses that were true for this graph. The only hypotheses from the list that we can apply to this graph are the one identifying clusters of target machines, which are called out with a rectangle, and the one where we are looking for outgoing connections, possibly identifying infected or compromised machines. All those cases are called out with a small circle.

Figure 5-27

Figure 5-27 Attack analysis graph with callouts that mark the attack hypotheses.

There seem to be two clusters of two machines each that have similar behavior. These machines very likely have a similar role in the network. Based on the fact that the clusters are small, it will not be spectacularly interesting to analyze the clusters anymore. If they were bigger, we should figure out what the exact IDS events were that targeted these machines. It seems more interesting to investigate the connections going to the outside. There is one particular instance that is called out with a big oval that seems interesting. The IDS observed an attack of highest priority targeting an internal machine. In addition, the IDS picked up another high-priority event that went back to the attacking machine. This is strange. Why would the internal machine trigger another high-priority event backout? This is definitely an instance that should be investigated!

Some of the hypotheses in the preceding list call for a new graph that needs to be generated to answer those questions. We need to get some additional data about the sessions, which we can gain from extended network flows. Figure 5-28 shows a graph that includes connection status information. The color of the nodes represents the connection status based on Argus output. In addition, the size of the target nodes encodes the number of times a connection was seen between the two machines. The input used to generate the graph is to extract the source, the destination, and the connection status from the Argus logs. The following is the AfterGlow configuration used to generate the graph:

color="gray" if ($fields[2] eq "RST")    # reset
color="gray20" if ($fields[2] eq "TIM")  # timeout
color="gray30" if ($fields[2] eq "ACC")  # accepted
color="gray50" if ($fields[2] eq "REQ")  # requested
# connected, finished, initial, closed
color="white" if ($fields[2] =~ /(CON|FIN|INT|CLO)/)
Figure 5-28

Figure 5-28 Attack analysis graph encoding the connection states and the number of times connections were seen between the machines.

With the graph in Figure 5-28, we can try to answer the questions 4 and 5 from the preceding list. Does the graph show any scanning activity? It seems like there are at least two clusters that look like scanners. One is situated in the middle, and one is on the left side of the graph They are both annotated as thick circles in the graph. Unfortunately, if we try to further analyze this, the graph is of limited use. I would like to know whether the connections that look like scanning activity were actually successful. Because multiple connections are overlaid in one node, however, the source node's color encodes the connection state of just one connection. It does not communicate the status for each of the connections, and the target nodes are too small to actually see the color on the nodes. To address this, we need to generate another graph and filter the original data to only show those nodes. The result is shown in Figure 5-29.

Figure 5-29

Figure 5-29 Zoom of the attack analysis graph, which only shows potential scanners.

To confirm the hypothesis that the two nodes in question are really scanners, we have to look at the connection status. The upper-left machine shows a lot of connections in the INT or REQ state, meaning that only a connection request was seen and never an established connection. Very likely, this is some machine that is scanning. On the other hand, the machine on the lower right seems to have only established connections. Most likely this is not a case of a scanner.

The good news is that all the connections the scanner (upper-left machine) attempted were unsuccessful. The part that causes concern is that there are some machines that are attempting to connect to the scanner machine. We should verify whether those connections were successful to assess the impact. It is not quite clear, without knowing the role of this machine, why other machines are trying to contact it.

What are some other things that we can identify in the original graph (refer to Figure 5-28)? The fifth hypothesis asked whether there were strange combinations of TCP flags in a session. This requires generating a graph that shows all the TCP flags for a session. With Argus, this is fairly simple. The following command will accomplish this:

ragator -nn -s saddr daddr dport bytes status -Z b -A -r log.argus - ip

The ragator command merges matching flow records in the Argus log (log.argus) and outputs the result on the console. The -Z b switch instructs Argus to output the individual flags of a session. Here is a sample output listing:     31      189   CON   652     3100  FSRPA_FSPA

The first record indicates that a connection was established, indicated by the CON flag. The termination of the session was not yet seen in the time frame observed. The second entry shows the summary of multiple sessions of which some were terminated with regular FINs, while some of the connections were terminated with a RST, hence the R in the output. Both outputs are absolutely normal and will commonly show up. A way to visualize this type of information is to use the packet counts as sizes for the source and target nodes and use the status flags to drive the color. This is similar to what we have been doing in the previous graphs. I leave it to the reader as an exercise to generate this graph and analyze it.

The sixth hypothesis is about analyzing packet sizes for different protocols. Each protocol has characteristic packet sizes. A DNS request, for example, should always be fairly small. If you see large packets as DNS requests, something is wrong. To analyze packet sizes, I am going to use a box plot. Figure 5-30 shows how the sizes of both requests and responses are distributed for each destination port shown in the log file. Note that the x-axis is displayed using a logarithmic scale. This helps to display the packet sizes, because a lot of the packets are fairly small and a few of them are really large. The side-by-side display of request and response sizes enables the comparison of individual services. You will need quite a bit of experience to interpret this graph. What are the packet sizes for all the protocols used on your network? How do requests and responses compare? To analyze the graph, apply heuristics. For example, HTTP (port 80) should have fairly small request sizes. If you see a lot of large requests, it probably means that unwanted data is being transferred in HTTP requests. On the other hand, HTTP responses are probably fairly large. As shown in Figure 5-30, this is exactly the case.

Figure 5-30

Figure 5-30 Box plot showing protocol size distribution for requests and responses of different protocols.

Operating System Log

The analysis of operating system log files yields some interesting use-cases and possibilities to further provide the insights necessary to find attacks. Operating system logs can be used in two cases: either to correlate them with other log sources or on their own. The first case is to use them to provide additional intelligence for network-based logs. For example, if an IDS reports a DoS attack against a service on a machine, the operating system logs can be used to verify whether that service indeed terminated and the DoS attack was successful. The other use-case for operating system logs is to use them on their own. By looking for interesting entries in the OS logs, you can often identify attacks, too. However, finding attacks with only OS logs is not always easy. As discussed in Chapter 2, not that many types of events are recorded in OS logs, and attacks are not specifically identified in OS logs.

How can an OS log be correlated with a network-based log, such as network flows? There are many ways to do so. The general idea is always that you either confirm or deny some activity that was discovered in the network-based data (especially when dealing with IDS events) or the OS logs are used to complete the picture and give more context. Often, this reveals interesting new information that the network-based logs alone would not reveal.

Correlating OS with network-based logs raises the challenge of "gluing" the two logs together. This is done through the IP addresses in the network logs. We need to combine the OS logs from a machine with the corresponding network-based log entries mentioning that specific machine. Let's look at an example to illustrate this process. The case I discuss shows network-flow data with SSH connections targeting one of our machines. A sample entry looks like this:

05-25-04 11:27:34.854651  *   tcp   ?>    149    172     14702      54360     CON

The flow shows that there was an SSH connection from to In the OS logs of the target machine (, we can find a log entry generated by SSH, indicating a successful login:

May 25 11:27:35 ram-laptop sshd[16746]: Accepted password for root from port 58841 ssh2

In addition, we have to make sure that the times of the log entries match. This is where it is important to have synchronized clocks! Figure 5-31 shows a graph where all network traces from SSH connections are shown. In addition, the SSH entries from the OS log are included in the graph. The benefit of looking at both traces is that we get a more complete picture. The network traces show all the SSH activity from all the hosts. In addition to that information, the OS log provides information from an operating system perspective. The OS logs contain the user that logged in and not just from which machine the login originated. This information can be useful. The example in Figure 5-31 utilizes the OS logs to show which users accessed our server ( The graph helps identify one machine,, which was used to log in to our server. The network traces help complete the picture to show all the SSH connections the originator machine attempted.

Figure 5-31

Figure 5-31 SSH activity, shown from both the network and the operating system perspective. This picture reveals that our critical server ( is not the only machine this user accessed.

Why did the source machine in Figure 5-31 attempt to connect to all these other machines? And worse, the login to the critical server was successful. It seems that the user account was compromised and the person behind all this activity, the one controlling the originator, should be investigated.

OS logs do not necessarily have to be correlated with network traces. Even by themselves, they provide a lot of value. In some cases, these use-cases need special configurations of the operating system to enable the necessary level of logging to record these instances. The following are some use-cases that OS log files can be used with:

  • Monitor file access of sensitive documents: To enable file auditing, have a look at Chapter 2. This can reveal users poking around in files that they have no reason to look at. Also, graph the number of files an individual user accesses, possibly even across multiple machines. This often reveals users who are snooping around.9
  • Monitor file access of configuration files: Chapter 2 shows how to set up file auditing. Configure all configuration files to be audited. Make sure there is a justification for every configuration change on your machines. Ideally, there is a capability to correlate configuration changes with trouble tickets that document the change and show the proper authorization.
  • Audit user logins: Look for logins per machine. Who are the users accessing machines? Graph the total number of logins per user. Do some users show suspicious numbers of logins?
  • Monitor listening sockets: Every new open port showing up on a machine needs to be investigated. There needs to be a good reason why a server suddenly starts listening on a new port. Again, you will hope that a trouble ticket justifies the new service. If not, why does a machine suddenly offer a new service?
  • Monitor performance-related parameters: By looking at CPU load, memory utilization, free disk space, and so on, you can not only detect performance degradations or machines that are starting to run at their limit, but performance related measures sometimes reveal interesting security-related issues. If a server is generally dormant during the night and suddenly shows a lot of activity, this might be a sign of an intrusion.

OS logs can prove useful in many more use-cases. This list serves merely as an inspiration. Visualization is powerful; through it, changes in behavior are easy to detect.

Application Logs

Going up the network stack, after the operating system we arrive at the application layer. There are many interesting use-cases where visualization is of great help in this space. The big difference from the previous discussion is that there are no generic use-cases. Instead, every application, depending on its logic, has its own unique analysis approaches. The following are some sample classes of applications that I briefly address to outline visualization use-cases:

  • Network infrastructure services, such as DNS and DHCP
  • Network services, such as proxy servers, Web servers, and email servers
  • Applications, such as databases, financial applications, and customer relationship management software

It would be instructive to consider many more classes of applications, but I picked these classes of applications because they can be correlated with network flows. I cover more application-based use-cases later in this book. Fraud is an interesting topic in the realm of application log analysis. I discuss the topic of fraud in Chapter 8, "Insider Threat."

The classes of applications listed here can all be used to visualize and detect problems in the application itself. DNS, for example, can be used to find unauthorized zone transfers. Again, I do not discuss these application-specific use-cases here, but pick them up later, for example in Chapter 6. What I am interested in, for the moment, is how application logs can help with some of the analyses we have done earlier. For example, can DHCP logs be used to facilitate or improve the analysis of network-based logs?

We can use many network infrastructure services such as DNS and DHCP to improve our network data visualizations. How can we use DNS logs? Well, DNS is about mapping host names to IP addresses and vice versa. Why would we use that information for visualization? One could argue that to resolve IP addresses into host names, you could just do a DNS lookup at the time the graph is generated. That would certainly work. However, we have to be clear about what exactly we are doing. We are resolving an IP address at a different point in time, and more important, we are probably resolving it with a different DNS server than the one that was used in the original network where the logs were captured. Imagine a network where a private address space is used. The DNS server for that network will be able to resolve the private IP addresses to a host name. If you try to resolve those addresses with a different DNS server, the IP will not resolve to the same host name. This is the reason we should use DNS logs to improve our analysis.

DHCP logs represent a similar source of data. They give us a way to map IP addresses to MAC addresses, or actual physical machines. MAC addresses are globally unique and therefore identify a machine uniquely. This can be useful in an environment where DHCP is being used. At the time of visualization, the IP address showing in the graph would most likely not be the same machine anymore as the one that was generating the activity. Ideally, you would also have an asset inventory at hand, which maps MAC addresses of machines to their owners. That way you have the capability to not just identify a specific machine responsible for a specific activity, but you could also identify a person responsible for the given machine. Applying all this data to a problem is fairly straightforward. The data can be used as a lookup table and is utilized to replace the IP addresses in the original logs.

How can we use network services, such as Web or mail servers, to aid in our analyses? Again, for this discussion, I am not interested in specific use-cases for these types of logs. Those are discussed in the Chapter 6. For the purposes of this analysis, I am interested in what additional information these logs can provide. Let's start with proxy logs. There are multiple types of proxies. Some are merely relays. The ones that I am interested in are proxies that require users to authenticate themselves. The logs from those proxies enable us to map IP addresses to users! This is incredibly interesting. Instead of identifying a machine that was causing mischief, we can now identify user names that are responsible for some activity, and we hope this will translate to actual humans. Note that this is not necessarily an easy task!

Do other network service logs result in similar benefits? The answer, as usual, is "it depends." All services that require a user login are potential candidates. We need to look for log entries that tie the user login to his or her IP address. ipop3d is a POP daemon that logs every session with a user name and client address from where the session was initiated:

Jun 12 09:32:03 linux2 ipop3d[31496]: Login user=bhatt host=PPP- [] nmsgs=0/0

If we extract the user and the user's IP address, we have an association of user to machine again. Other services provide similar log entries that help associate users with machines. With some logs, it is possible to associate a machine not just with a login but also with an email address. Fairly obvious, mail servers are candidates for this. One of them is Sendmail. Be careful with mail server logs. They are among the worst logs I have ever seen. Instead of logging on a session level, mail servers often log on an application logic level. For example, Sendmail logs a message as soon as the server gets an email that has to be delivered. At that point, Sendmail logs that it got a message from a certain email address. It does not yet log to whom the messages was addressed. Only after the email is ready to be delivered will it log that information. This makes it incredibly hard for us. We have to manually stitch those messages together. Mail processors generate even worse logs, logging every individual step during the mail processing and always logging just a piece of the complete information that we would need for visualization.

The only class of information that we have not looked at yet is desktop applications. The challenge with desktop applications is that they do not allow logins over the network. Therefore, the log files do not contain IP addresses that we could use to correlate the information with network traffic. One possible use of application logs is to use them to gain more information about users and their roles. We will run into various problems trying to do that, however. Assuming that the user names are the same among applications and network services, we can try to look for events related to the same user's activities and glean information from the application log entries. Most likely, however, the flow is the other way around. You will have to use application logs as the base and augment the information with network layer information. This will enable you to correlate activity in applications with the origin of that activity. This can help to verify what other activities a certain user is involved in. Is someone executing a certain transaction on an application while at the same time using a network service? An example application is the supervision of financial traders. If they are placing a trade shortly after receiving a call via Skype or an instant message from a machine outside of the corporate network, it is possible that they got a tip from a third party.

The graph in Figure 5-32 shows what a simple, fake scenario could look like. The left part of the graph shows all instant messenger traffic. You can clearly see that the traders are communicating with each other. However, one machine seems to be an outlier. It looks like an instant message originated from the gateway address. This could indicate that an external message was received. This might be important to know. This by itself might indicate a policy violation. To see whether this is really something to investigate, we need the IP address to do an owner association. In addition, we want to see the trades themselves. Some example trades are shown in the figure. We see the user who posted the trade, the accounts involved, and the amount transferred. The rightmost graph in Figure 5-32 shows a graph where all this information is merged together. The IM traffic is plotted but with the nodes changed to the machine's owner rather than the IP addresses. In addition, the nodes are colored based on the transaction volume that each of the users traded. The darker the node, the more money was traded. We can see that the user who received an instant message from an external address was not the one posting the biggest trade. In addition to this graph, it would be interesting to see a timetable that shows the timing sequence of how the trades relate to the instant messages. I leave it to you to imagine what such a graph would look like.

Figure 5-32

Figure 5-32 An example where an application log was correlated with network behavior based on the user names in the log files.

Additional Data Sources

A number of data sources are not covered in the process I just outlined. This does not mean that they are less useful. On the contrary, they could provide some significant insight into the behavior the logs recorded. Additional data comes not only from devices that generate real-time log files, but also from completely different sources, such as statically updated spreadsheets. Information such as the role of machines on the network is often managed in spreadsheets. Only a few networks that I have seen are actually documented in a configuration management database (CMDB) or in an asset management tool. This is unfortunate because it would make our analyses much easier if we had access to up-to-date data from CMBDs. Other information is important, too, such as policies. Which machines should have access to which other machines? Which user roles have access to which machines? One common use-case is that you want to allow only users in the DBA (database administrator) role to use the DBA accounts to work on the database. You want to prevent every other user account from using this account. To do so, you need role information for the users. This information can often be found in a directory, such as LDAP or Active Directory.

How do you use this information in the analysis process? Ideally, the data sources are used as overlays to the existing graphs. In some cases, it will enhance the accuracy and the ease of analyzing log files by providing more context. In other cases, these additional data sources spawn an entire set of new applications and detection use-cases. We saw one application of additional data in the previous section on application logs, where I mapped IP addresses to their respective owners. Other use-cases are similar to this. What are some additional data sources that we should be looking at? Here is a short list:

  • Vulnerability scanners: They can help with filtering out false positives from IDSs and factor into the priority calculation for the individual events. Make sure you are not just getting the vulnerabilities for machines but also the open ports and possibly some asset classification that has been used in the vulnerability management tool.
  • Asset criticality: This information is often captured in spreadsheets. Sometimes the vulnerability management tool or an asset management database can provide this information. It is not always necessary to have a criticality for each machine on the network. Knowing which machines are the highly critical ones is typically sufficient.
  • User roles and usage policies: User roles can be collected from identity management stores or possibly from logs that mention role changes. This information can be useful, especially in conjunction with policy modeling. If you can define usage policies, this will enable the monitoring of user activity with regard to respective roles. Things such as engineers accessing an HR server or salespeople accessing the source code repository become fairly easy to express and monitor. Policies are not restricted to IP addresses and who can access machines. They can extend to the application layer where the definition of acceptable behavior inside of applications becomes possible.
  • Asset owners: Generally, this type of information is found in spreadsheets. Sometimes an asset management or a CMDB is available that stores this type of information. The information is useful for mapping IP addresses to machines and perhaps even to the owners responsible for those machines.

You can use this information in multiple ways for visualization. It could be used to replace values with ones that are looked up in these sources, as we have done in Figure 5-32, where we replaced the IP addresses with the respective owner of the machines. Another way is to use them for color assignments or you could even use the owner and explicitly use it as an additional data dimension in the graph.

This concludes the discussion of the attack detection process. We have seen how we can forensically analyze log files and apply graphs to simplify the process. Unfortunately, the process does not guarantee the detection of attacks that have happened. It is merely a tool to help analyze the logs files for any suspicious signs and to uncover potential problems, or in some cases, attacks. The next section examines how to assess an attack. This is slightly different from what we have done so far. Instead of looking for the attack, the premise is that you know that there was an attack, possibly even knowing what some of the affected machines are or how the attack was executed.

Assessing an Attack

A fairly different use-case compared to detecting attacks in log files is the assessment of a successful attack. An attack can be detected in a number of ways, be that through the attack detection process or, for example, a customer who called in and reported an issue that could be as benign looking as a performance problem or a service doesn't work anymore. The assessment of the attack impact and extent is important to know what was affected and how big the loss is. It is also necessary to understand how the attacker was able to penetrate the systems and in turn how to prevent similar attacks in the future.

The following discussion applies mainly to cases where more complex attacks are executed. Typically, those attacks involve a network component. If the attack affects only one host and is executed locally on the machine, visualization of log files cannot help much in this scenario. However, if an attack is executed over the network and potentially involves multiple machines, there is a chance that visualization can help shed some light on the details of the attack, how pieces are related, and so on. I call this analysis attack path analysis. I would like to understand how an attacker was able to enter the network, what he touched, and so forth.

To start the process, we start with data gathering. As soon as the attack assessment process is started, we need to begin collecting pertinent log files. I hope that a variety of logs are available: network flows, intrusion detection data, firewall logs, host logs, and so on. We should extract a period of time preceding the attack. It might be enough to go back an hour. Depending on the type of attack, it is possible that not even a day is enough; instead, an entire year might have to be analyzed. When you start analyzing the attack, you will fairly quickly understand the time frame that you are interested in. Let's start with just an hour. Most likely, you will know what machine was attacked. Extract records just for this machine (or machines, if multiple ones were affected). Most likely, your problem at this point will be that you don't know what the source of the attack is. Therefore, finding the source of the attack is going to be the first analysis objective. How do you do that? It might be close to impossible if the attacker executed the attack carefully enough. However, most likely the attacker made a mistake somewhere along the line. One of those mistakes could be that the attacker tried to access services on the target machine that are not available. Another possibility of detecting an attacker is the number of interactions he had with the target machine. Use a bar chart to show how many connections each of the sources opened to the target machine. Anything abnormal there? Use some other techniques of the attack analysis process to see whether you can find anything interesting that might reveal the attacker.

After you have identified potential attackers, use this candidate set to analyze all the activity seen by these machines. Most likely, a link graph will prove useful to show the relationships between all the attack candidates and the target machine. At this point, it might prove useful to extend the analysis window and take more data into account to see what the source machines have touched over a longer period of time. This will give you a good understanding of the extent of the attack. Which machines were affected and what services were used?

Especially with the information about the services the attackers used, you can verify the host logs to see whether you find any clues about how exactly the attackers entered the systems. The goal should be to gain a clear understanding of how the attack worked, who the attackers were, and which machines were involved. And more important than that, you should have a clear understanding of what data was affected!

In the next step, you can try to design a response to this attack. There are many possibilities:

  • Blocking this type of traffic on a firewall between the attackers and the target machine
  • Patching the vulnerabilities on the end system that the attackers exploited
  • Introducing additional levels of authentication
  • Deploying automatic response capabilities to block such attacks in real time

Visualizing the attack path is probably one of the most useful tools. It can help you not just analyze and understand the attack, but it also helps communicate the attack to other teams and eventually to management. Let me summarize the individual steps again that I went through to assess the attack:

  1. Get records for the affected machine.
  2. Find the source of the attack by looking for strange access patterns (e.g., connections to ports that are not open, excessive number of connections, strange behavioral patterns).
  3. For the sources identified to be the potential attackers, analyze all the data referencing them. Find which machines they touched.
  4. Deploy countermeasures to prevent similar attacks in the future.
  5. Document the attack in detail (see the next section).

It would be interesting if you had the capability to execute all these steps in near real time. Doing so, you could prevent the attacks from happening. The part about responding to the attack and putting mitigation capabilities into place especially benefits from quick turnaround times. Commercial systems are available to help with these steps.

Documenting an Incident

The last part of forensic log visualization is the documentation of an incident. The attack detection process helped us find an attack and identify it. The second part was about assessing the impact and extent of an attack. When that information is known, we generally have to document the incident and provide this information to management, possibly law enforcement; and in a lot of cases, we can use the information gathered to educate people in our organization. Only part of incident documentation can be done with log files. Often, forensic images will be taken from machines that will serve as evidence and documentation of an incident. However, the part that can be done through log files is often useful to help communicate how an attacker entered the systems and the extent of the problem.

I have already discussed a lot of the elements of incident documentation when we talked about reporting. The important things to keep in mind are two things:

  • Who is the audience for the incident documentation?
  • What is the best way to represent the information?

These two questions will help you make sure that the documentation meets its goals. If you are writing the documentation for your management, make sure that you show on a higher level how the attack happened. Why was the attacker able to get in? Don't go into all the gory details of the vulnerabilities that were present and the exploit code that was used. Show concepts and where more controls could have prevented the attack. If you are communicating the information to the owner of the servers that were penetrated, mention all those gory details. Help them understand how the attacker penetrated the system, but don't forget to mention how the attack could have been prevented. The system administrators will not be too interested in the network components of the attacks but instead will want to know what happened on the server itself.

It is not always the best idea to use graphs and visualization for this type of documentation. Some of the documentation is better communicated in textual form. However, if you are trying to show an attack path, how the attacker actually entered the system, a picture is still worth a lot. Show the network topology, and on top of it, how the attacker was able to come in. Link graphs are generally a great tool for this type of documentation. I could spend a lot more time on this topic, but it is not one that benefits tremendously from visualization. In sum, if you can summarize information in a graph to communicate the pertinent information with other people, do so!

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