2.5 DDoS: Hype or Reality?
The issues described in the previous section make DDoS attacks a frightening possibility. Yet researchers in computer and network security are aware of many frightening possibilities that never come to pass. Are security researchers merely alarming the public with claims of the dangers of DDoS?
Unfortunately, DDoS attacks are not speculation or fiction. They occur on a daily basis, directed against a wide range of sites. Chapter 3 details, in timeline fashion, a large number of representative attacks. The details of these attacks will be left to that chapter, while some specifics will be mentioned in this chapter. In addition to several well-known occurrences of DDoS attacks that were widely reported in the press, there are scientific studies of the frequency of these attacks that demonstrate the reality of the problem (see Appendix C for a summary of these studies).
2.5.1 How Common Are DDoS Attacks?
There are some forms of cyberattacks that receive a lot of publicity because they generate a few high-profile incidents, even though these types of attacks do not actually occur that often. Unless these incidents are particularly disastrous, the overall impact of the attacks is more related to publicity than large amounts of damage done to many businesses or individuals.
DDoS attacks do not fit that category. A number of recent studies have demonstrated that DDoS attacks are extremely common in today's networks. Given that they are usually quite effective and perpetrators are rarely caught, there is reason to believe they will become even more popular in the future.
Measuring the frequency of any form of attack in the Internet is difficult. Victims do not always realize that they are under attack. Even if they do, they often fail to report the attack to any authority. A number of organizations use survey techniques to gain some insight into the prevalence of different kinds of cyberattacks and the amount of damage they do. One example is the FBI's annual report on cybercrime, based on information provided by nearly 500 organizations. In the 2004 report, nearly a fifth of the respondents who suffered financial loss from an attack had experienced a DoS attack. The total reported costs of DoS attacks to these companies was over $26 million. Denial of service was the top source of financial loss due to cybercrime! These surveys are often criticized because their methodology is unavoidably subject to certain limitations, but relatively little better data exists.
The methods used in these surveys do not differentiate between distributed and nondistributed DoS attacks, since the technology for making the distinction is in its infancy. In the meantime, researchers have used a variety of techniques to estimate data on the frequency of DDoS attacks and their other characteristics.
For example, Farnam Jahanian of the University of Michigan has been able to observe network activities in the MichNet ISP. This network provider offers ISP service to government and nonprofit organizations in the state of Michigan, including most educational institutions in that state. Over the course of time, Jahanian's team has gathered data suggesting that DDoS attacks are quite common and are increasingly sophisticated. Jahanian's full results have not yet been published; however, a presentation covering some of his results can be found at http://www.arbor.net/downloads/nanogSlides4.pdf.
A number of researchers have investigated various technical means to deduce information about the prevalence and character of DDoS attacks in the Internet [DLD00]. CAIDA (the Cooperative Association for Internet Data Analysis), for example, used a technique called backscatter. Full details of this technique and CAIDA's results can be found in Appendix C. Their results suggest that during a three-week observation period in 2001 there were around 4,000 DDoS attacks per week on Internet nodes.
For reasons covered in Appendix C, CAIDA's numbers are certainly an underestimate. Jahanian's results can be interpreted to suggest that the CAIDA figure of 4,000 attacks per week would be more realistically set at 12,000 attacks per week, even leaving aside some classes of DDoS attacks. Further, other data suggests that DDoS attacks have become more common since 2001.
If DDoS attacks are so common, why do we not hear more about them? Evidence gathered by CAIDA and Jahanian suggests that most DDoS attacks are launched against fairly small targets (home machines, for example) for short durations. Some have speculated that many of the incidents represent hackers attacking each other, though too little evidence exists to come to any strong conclusion on this point. Short durations can cause a DDoS attack to appear to be no more than another network glitch. When a user clicks on a link and receives no response for a minute or two, he is more likely to conclude that the server is busy or that there are general network congestion problems, rather than that he (or, more likely, the server) is suffering a DDoS attack. Thus, in many cases DDoS attacks may pass unnoticed.
If many DDoS attacks are not even noticed, how seriously should we regard the problem? First, there is a significant and growing number of high-profile incidents of serious, persistent, powerful DDoS attacks clearly meant to deny service to important sites. Second, remember that the small, short attacks are typically small and short because that was what the attacker wanted to do, rather than what he could do. A DDoS agent network can continue its attack for hours, or perhaps even indefinitely. And attackers can easily gather huge agent armies. The techniques are already well known and of proven effectiveness. All that remains is a sufficient motive for them to be widely used for destructive purposes.
2.5.2 The Magnitude of DDoS Attacks
Another potentially measurable dimension of a DDoS attack is its size. The size of an attack can be measured in the traffic it generates or in the number of sites participating in the attack. It can also be measured in its duration, a characteristic that some DDoS studies have addressed.
The built-in statistics capabilities of the Shaft attack tool [DLD00] allowed researchers to estimate the magnitude of a given attack in late 1999, at 4.5 Mbps emanating from a single DDoS agent in a network of about 100 agents (see also Figure 4.11 in Chapter 4). Also, MultiRouter Traffic Grapher (MRTG) measurements [Oet] from an actual attack in May 2001 collected close to the target location provide a lower estimate for the inbound attack traffic volume of about 25 Mbps (see Figure 4.13 in Chapter 4). The lower estimate is due to the measurement equipment collapsing intermittently under the heavy load.
DDoS attacks that have taken out large network links in the past, such as an attack on Australian Uecomm, have involved volumes of up to 600,000 pps [Gra]. In attacks on the DNS root servers in 2002, each server received 100,000 to 200,000 pps [Nar]. In some cases, such as the Al-Jazeera attack in 2003, the attackers added attack volume as the defenders added capacity to handle traffic. This shows that attackers can easily increase the attack strength when necessary, so the measured attack magnitudes have more to do with what the attacker feels is required than with the maximum amount that he can generate. In fact, many attacks may have specifically used a set of moderate-sized discrete attack networks so as to not expose all of them at one time. More recent attackers have learned that it is wasteful to use all of their resources at one time and instead ramp up an attack slowly to maximize how long the attack can be maintained in the face of attrition of the agents.
The backscatter approach used by CAIDA can also estimate the volume of attacks. (Again, for details on how this can be done, see Appendix C.) Taking into account certain limitations of the approach that might lead to underestimates, half of the attacks they observed caused volumes of 350 pps or more. Depending on the target's capabilities, the type of packet, and the target's defenses, this volume is often enough to deny service. The largest volumes CAIDA deduced were hundreds of thousands of packets per second. For example, in the TCP SYN flood attacks against SCO in December 2003, CAIDA estimated that SCO's servers received as many as 50,000 pps at one point and dealt with a total of over 700 million attack packets over a 32-hour period. They estimated this peak rate of 50,000 pps yielded "approximately 20 Mbits/second of Internet traffic in each direction, comparable to half the capacity of a DS3 line (roughly 45 MBits/second.)" [MVS01].
In terms of the number of machines involved in an attack, statistics are harder to come by. It is clear from evidence gathered by the University of Minnesota, which suffered one of the first DDoS attacks in 1999, that DDoS attack networks could be assembled from well over 2,200 systems using only partially automated agent recruitment methods. This minimum number is known because that attack did not use IP spoofing. In attacks in which some form of IP spoofing is used, merely counting the number of IP addresses observed during a particular DDoS attack will grossly overestimate the number of nodes involved.
Another approach is to deduce the number of machines from the observed volume. The largest attack rate observed by CAIDA was estimated to be 679,000 pps. How many packets a machine can generate per second depends on several factors, including its CPU speed and network connectivity. For machines with 10-Mbps links to the Internet, generating 20,000 pps is probably near their maximum capability. So if we assume the largest attack observed by CAIDA was performed by a group of such machines, there had to be at least 30 or 40 of them. For the DNS server attack mentioned above, there had to be at least 90 of them. Many machines have substantially lower speed Internet connections, and if these machines are used as agents, many more of them would be required to achieve these rates. For example, if all agents used 56 Kbps links to connect to the Internet, CAIDA's largest observed attack would have involved at least 5,800 agents. The actual number of agents used in this attack is probably between these ballpark figures. Reflected attacks, where attacking hosts send out forged attack packets that are reflected off a very large number of legitimate servers around the world, greatly amplify the attack. One such attack against futuresite.register.com involved a very small number of attacking hosts, but was still able to generate 60 to 90 million bits per second flooding the victim.
One might wonder where the DDoS agents come from. Most experts believe that very few attackers use their own machines to launch DDoS attacks, since doing so would increase their risk of being caught. Instead, they compromise other machines remotely and use them to launch the attack. If compromising a remote machine were a difficult process requiring extensive human intelligence and attention, this factor would limit the seriousness of the DDoS threat. However, experience has shown that automated techniques are highly effective at compromising remote sites, which can then be used to launch DDoS attacks.
Just to give an idea of how easy it is to compromise a large number of hosts, here are some figures:
- Microsoft announced that their MSBlast cleanup tool was downloaded and used to successfully clean up 9.5 million hosts from August 2003 to April 2004, an average of approximately 1 million compromised computers per month (see http://zdnet.com.com/2100-1105-5201807.html?tag=nl).
- Microsoft announced in May 2004 that they had cleaned up 2 million Sasser infected hosts (see http://www.securityfocus.com/news/8573).
- The same news story reports Symantec had identified a bot network of 400,000 hosts.
- A network administrator in the Netherlands has identified between 1 million and 2 million unique IP addresses associated with Phatbot infections. Phatbot has features to harvest MyDoom- and Bagel-infected hosts, among other infection vectors (see http://www.ladlass.com/archives/001938.html).
Probably the most common method of recruiting agents is to run an automated program that scans a large IP address range attempting to find machines that are susceptible to well-known methods of compromise. These programs, called automated infection toolkits, or auto-rooters (after the name of the system administrator account on Unix systems, root, also the hacker verb meaning "to compromise or gain elevated privileges on"), are generally quite successful in finding large numbers of vulnerable machines, particularly if they are updated to include newly discovered vulnerabilities that are less likely to have been patched.
The ultimate in automation is an Internet worm—a program that looks for vulnerable machines and infects them with a copy of its code. Worms propagate extremely rapidly. Some worms have used their armies of infected machines specifically to perform DDoS attacks. The worm can even carry the code to perpetrate the DDoS attack. For example, Code Red was designed to perform a DDoS attack from all the nodes it compromised on a particular IP address. Code Red succeeded in infecting over 250,000 machines, by some estimates. Code Red II infected as many as 500,000 machines. Estimates for the number of machines infected by the W32/Blaster and W32/Sobig.F worms run from the tens of thousands to a few hundreds of thousands, and some reports refer to these numbers as "small." Sasser infected at least 2 million hosts, judging by Microsoft's report (http://www.securityfocus.com/news/8573). Thus, it is quite realistic to envision DDoS attacks originating from hundreds of thousands, even millions of points in the Internet.