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Detecting intrusion using system calls: alternative data models (1999)
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| Venue: | In Proceedings of the IEEE Symposium on Security and Privacy |
| Citations: | 433 - 3 self |
BibTeX
@INPROCEEDINGS{Warrender99detectingintrusion,
author = {Christina Warrender and Stephanie Forrest and Barak Pearlmutter},
title = {Detecting intrusion using system calls: alternative data models},
booktitle = {In Proceedings of the IEEE Symposium on Security and Privacy},
year = {1999}
}
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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. 1.
Keyphrases
alternative data model relative frequency illegitimate activity intrusion detection system following method particular problem system-call data set simple enumeration wide variety hidden markov model operating system observable data different data different sequence several different program observed sequence rule induction technique normal behavior observable sequence
