Polymorphic Code Detection with GA Optimized Markov Models

This paper presents our progression in the search for reliable anomaly-based intrusion detection mechanisms. We investigated different options of stochastic techniques. We started our investigations with Markov chains to detect abnormal traffic. The main aspect in our prior work was the optimization of transition matrices to obtain better detection accuracy. First, we tried to automatically train the transition matrix with normal traffic. Then, this transition matrix was used to calculate the probabilities of a dedicated Markov sequence. This transition matrix was used to find differences between the trained normal traffic and characteristic parts of a polymorphic shellcode. To improve the efficiency of this automatically trained transition matrix, we modified some entries in a way that byte-sequences of typical shellcodes substantially differs from normal network behavior. But this approach did not meet our requirements concerning generalization. Therefore we searched for automatic methods to improve the matrix. Genetic algorithms are adequate tools if just little knowledge about the search space is available and the complexity of the problem is very hard (NP-complete).