Modeling of Network Intrusions Based on the Multiple Transition Probability
In the TCP network environment, all unit transmissions are constructed using sessions. In the session, packets are transmitted sequentially. In this case, the previous and next packets contain causality mutually. Thus, we propose a method that models network transmission information based on transitions of packet states. In addition to the transition model, a probability matrix for the multiple state-transition models of all sessions is represented. The matching of the models is achieved using the maximum log-likelihood ratio. Evaluation of the proposed method for intrusion modeling is conducted by using 1999 DARPA data sets. The method is also compared with Snort-2 which is misuse-based intrusion detection system. In addition, the techniques for advancing proposed method are discussed.
KeywordsNetwork-based intrusion detection multiple transition probability Ergodic model probability-based modeling likelihood measure
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