Internet worms have become a widespread threat to system and network operations. In order to fight them more efficiently, it is necessary to analyze newly discovered worms and attack patterns. This paper shows how techniques based on Kolmogorov Complexity can help in the analysis of internet worms and network traffic. Using compression, different species of worms can be clustered by type. This allows us to determine whether an unknown worm binary could in fact be a later version of an existing worm in an extremely simple, automated, manner. This may become a useful tool in the initial analysis of malicious binaries. Furthermore, compression can also be useful to distinguish different types of network traffic and can thus help to detect traffic anomalies: Certain anomalies may be detected by looking at the compressibility of a network session alone. We furthermore show how to use compression to detect malicious network sessions that are very similar to known intrusion attempts. This technique could become a useful tool to detect new variations of an attack and thus help to prevent IDS evasion. We provide two new plugins for Snort which demonstrate both approaches.
This paper describes an approach to detecting distributed denial of service (DDoS) attacks that is based on fundamentals of Information Theory, specifically Kolmogorov Complexity. A theorem derived using principles of Kolmogorov Complexity states that the joint complexity measure of random strings is lower than the sum of the complexities of the individual strings when the strings exhibit some correlation. Furthermore, the joint complexity measure varies inversely with the amount of correlation. We propose a distributed active network-based algorithm that exploits this property to correlate arbitrary traffic flows in the network to detect possible denial-of-service attacks. One of the strengths of this algorithm is that it does not require special filtering rules and hence it can be used to detect any type of DDoS attack. We implement and investigate the performance of the algorithm in an active network. Our results show that DDoS attacks can be detected in a manner that is not sensitive to legitimate background traffic.