Detecting intrusions using system calls: alternative data models

Christina Warrender, Stephanie Forrest, Barak Pearlmutter

Research output: Contribution to journalConference articlepeer-review

399 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)133-145
Number of pages13
JournalProceedings of the IEEE Computer Society Symposium on Research in Security and Privacy
StatePublished - Jan 1 1999
Externally publishedYes
EventProceedings of the 1999 IEEE Symposium on Security and Privacy - Oakland, CA, USA
Duration: May 9 1999May 12 1999

ASJC Scopus subject areas

  • Software

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