The beginning of this book introduced all the building blocks necessary to generate graphs from security-related data. I have discussed some of the data sources that you will encounter while analyzing security data. The discussion showed what information each of the sources records and what some of the missing information is. I discussed the different graphs and how to most effectively apply them to your problems, and after that I introduced the information visualization process, which guides you through the steps necessary to generate meaningful visual representations of your data. As the last step of the information visualization process, I briefly touched on the analysis and interpretation of the graphs generated.
This chapter elaborates on that concept and shows different ways of analyzing security data using visual approaches. I separate the topic of graph analysis into three main categories:
- Historical analysis
- Real-time monitoring
Reporting is about communicating and displaying data. Historical analysis covers various aspects of analyzing data collected in the past. The motivations and use-cases are manifold. They range from communicating information to investigate an incident (or other type of problem) to analyzing the data to comprehend the underlying data and situations reported. We discuss the topic of historical analysis by separating it into three subcategories:
- Time-series visualization
- Correlation graphs
- Interactive analysis
- Forensic analysis
After looking at historical analysis, we follow up with real-time monitoring, which, in the context of visualization, heavily uses the concept of dashboards. I discuss some of the main criteria for building effective dashboards for communicating security data. A topic closely related to dashboards is situational awareness. I discuss the importance of real-time insight into key security-relevant areas and how it can be used to drive decisions and react to, or proactively address, upcoming problems.
Each of the four historical analysis sections focuses on analysis. This material is not about how specific sets of data can be represented in a graph; we have already discussed that in the beginning of the book. Here the focus is on how to represent different sets of data to analyze and compare them. What are some of the common ways of analyzing security data? In some cases, we will see that the analysis requirement will influence how to create visualizations of the individual datasets, creating a closed loop with the graph-generation process.
One of the most often used techniques to communicate and display data is the report. Reports are not the prime example for showing the advantages of visualization. However, visualization in the form of graphs is a commonly used tool to improve the effectiveness of reports. Reports are a great tool for communicating and summarizing information. Reporting ranges from status reports, which show, for example, the type of network traffic the firewall blocked during the past seven days to compliance reports used to prove that certain IT controls are in place and operating.
A report can consist of just text, a simple graph, a combination of a graph and text, or a collection of graphs with optional text. A report based only on text is not something I discuss here. This book is concerned with visual communication of information and not textual encoding. One of the key properties of a report is that it focuses on past information. Reports are generated ad hoc, when needed, or on a scheduled basis. For example, I am using a weekly report to see the amount of blocked traffic per protocol that targeted my laptop. The report is emailed to me so that I get a feeling for what the attack landscape looked like during the past week. Instead of using textual information, I am using graphs to summarize the data visually. Figure 5-1 shows an example of such a report.
Figure 5-1 A sample report of traffic targeting my laptop. The charts show protocol distributions, the top attackers, and the number of blocked traffic incidents over the past seven days.
The prevalent data source for reports is a database. The advantage of having the data in a database—over, for example, a file—is that SQL can be used to process the data before the report is generated. Operations such as filtering, aggregating, and sorting are therefore easy to apply. In some cases, reports can be generated directly from log files, which I did for the report in Figure 5-1. However, this might require some more manual data processing. The one type of data source that is not well suited to reporting is real-time feeds. Reports are static in nature and represent a snapshot in time. In contrast to reports, dashboards are designed to deal with real-time data. More about dashboards a little later. Apart from the real-time data sources, most other data sources are well suited to reporting.
The goal of a report is to communicate information. The audience should be able to read the report and understand immediately what is shown. It should not be the case that additional information or explanations are needed to understand a report. Therefore, graphs that are complicated by nature are not well suited to reports. Simple graphs are preferable. That is why bar charts and line charts are great candidates for inclusion in reports. Sometimes scatter plots or time tables can be used, too. All the other graphs, such as link graphs, treemaps, parallel coordinates, and all three dimensional graphs, generally need more explanation or the capability for the user to interact with the graph to make it effective and useful. Bar charts and line charts are by far the most familiar graphs. Everybody has seen them used for many different data visualizations. There are, as always, exceptions to the rule. In addition to choosing the right graph to visualize your data, make sure that you apply the graph design principles with regard to size, color, shape, data-ink ratio,1 and so on to make sure the graphs are easy to read.
Tools to generate reports can be divided into three main categories. The first category consists of security reporting solutions, such as security information management (SIM) and log management tools. These solutions are capable of not just generating reports, but also taking care of all the processing to get the data into a report, such as collection, normalization, storage, and so forth. These tools focus on security events and generally ship with a set of predefined reports for specific reporting use-cases. Unfortunately, most of these tools are not cheap. Those SIM tools available as open source are limited in their capabilities and generally lack adequate support for data feeds.
The second category consists of general-purpose reporting solutions. Microsoft Excel and OpenOffice spreadsheets, Crystal Reports, Advizor, and gnuplot fall into this category. These tools do not deal with data collection. In addition, these types of tools are not built for security data and therefore might not offer some of the functionality necessary. For example, functions to format or process IP addresses are generally not available. However, these tools offer a great variety of graphic capabilities and are generally easy to use and operate. Other drawbacks that you might find annoying fairly quickly are that they operate on static data and that the generation of a new report cannot be automated.
The third category consists of programming libraries. There are dozens of such libraries, both commercially available and open source. Most libraries support the common programming languages, such as Java, PHP, and Perl. In Chapter 9, "Data Visualization Tools," I discuss some of the open source libraries that can be used to generate reports. One of the libraries I use fairly often is ChartDirector, which is available at www.advsofteng.com. The great benefit of libraries is that you can script the generation of reports and embed them into your own tools. This makes libraries the most flexible tool for report generation. You might pay for the flexibility because of the learning curve associated with working with the library and building the coding framework to use it.
Issues and Problems
What are some of the problems or issues to watch out for when generating reports? One ubiquitous challenge is that too much data is available. It is important to filter and aggregate the data meaningfully and then apply the right graph type to represent the data. Doing so will help prevent cluttered graphs and make sure large amounts of data are dealt with efficiently. A point that I cannot stress enough about the entire world of visualization is that we have to keep the audience in mind with every decision we make. Who is going to look at the graph? A technically savvy person? A business person? If I generate a report for myself, I don't have to add much meta data for me to correctly interpret the graph. After all, I have been the one generating the report. I should know what is in it. However, if I were going to generate that same report for someone else, I would likely have to add some meta information so that the other person could understand the report.
Reporting Machine Access—An Example
Let's take a look at two sample charts that are frequently used to report machine access. Figure 5-2 shows a user login report where the number of logins is indicated for each user. The horizontal bars, rather than the normal vertical ones, helps keep the labels legible. Especially for long labels, this solution yields better results. The information for this type of graph can be collected from operating system or application logs. It would not hurt to even combine those two types of logs (operating system and application) into one single chart. The chart gives you insight into the behavior of the most active users, as well as the general user population accessing machines. Many things can be identified easily with this chart. For example, you might want to look out for direct root access. Good security practice is that root should never directly access a machine; instead, sudo or su should be used to execute commands that require root privileges. Also look for users who show an abnormally high login count. Pay attention in particular to the dark portion of the bars encoding the failed logins; they should not be too long. You should be alarmed if the number of failed logins is almost 50 percent compared to the total number of logins, as is the case for a couple of users in Figure 5-2. Why would there be so many failed logins? Depending on your exact setup and the machines or applications for which you are collecting login information, you might want to look out for other things. It might even make sense to configure the graph to highlight some of those instances with a special color (for example, the root logins, as shown in Figure 5-2).
Figure 5-2 A sample report showing the number of logins per user. The chart also encodes whether the logins were successful and where they failed.
Continuing with login information, in some cases you are not interested in the distribution of logins based on users, but you need to know the distribution per machine. This is shown in Figure 5-3. The chart encodes failed logins with black bars, and the bars are sorted to show the most failed logins on top. The second bar from the top clearly sticks out. Why is the percentage of failed versus successful logins for this machine about 90 percent? This is probably a case worth investigating.
Figure 5-3 A sample report showing the number of logins to various machines. The chart uses color to encode whether the login succeeded or failed.
These reports are great tools to communicate among different teams. Security analysts frequently deal with log files and are comfortable with textual log data: syslog, packet captures, and so on. Using these logs to communicate with other teams, such as the operations team, is not very efficient. You will quickly realize that the operators are generally not keen on even trying to understand the log files you send them. Using graphical reports instead can work wonders. I know of an example where the security team used to hand log files to the operations people whenever a worm-infected machine was found, with little effect. The operators took their time trying to understand what the log files documented, and in a lot of cases returned them with a note that they were not able to find the problem. Upon the introduction of graphical reports, requests to clean worm-infected machines were handled in record time. People understood the reports. It is easy to make claims in a graphical report that even management understands without further explanation. Figure 5-4 shows a sample graph that illustrates this scenario.
Figure 5-4 A sample graph showing the number of failed logins to various machines per user.
During the analysis of the last three charts, you might have felt an urge to compare the logins to an earlier state. How do the logins of this week compare to the ones of last week? This is where historical analysis, which I discuss in the next section, comes into play.