Warrender , Christina and Forrest , Stephanie and Pearlmutter, Barak
Detecting intrusions using system calls: alternative data models.
In: Proceedings of the 1999 IEEE Symposium on Security and Privacy, May 9-12, 1999, Oakland, California.
Intrusion detection systems rely on a wide variety of observable data to distinguish between legitimate and illegitimate activities. In this paper we study one such observable—sequences of system calls into the kernel of an operating system. Using system-call data sets generated by several different programs, we compare the ability of different data modeling methods to represent normal behavior accurately and to recognize intrusions. We compare the following methods: Simple enumeration of observed sequences, comparison of relative frequencies of different sequences, a rule induction technique, and Hidden Markov Models (HMMs). We discuss the factors affecting the performance of each method, and conclude that for this particular problem, weaker methods than HMMs are likely sufficient.
Conference or Workshop Item
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||Authorisation; Hidden Markov models; Knowledge based systems; Operating system kernels; Safety systems; HMMs; Alternative data models; Data modeling methods; Illegitimate activities; Intrusion detection systems; Legitimate activities; Normal behavior; Observable data; Observed sequences; Operating system kernel; Relative frequencies; Rule induction technique; Simple enumeration; System calls; System-call data sets.
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