Intrusion Detection: A Bioinformatics Approach

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Intrusion Detection: A Bioinformatics Approach (2003)

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by Scott Coull , Joel Branch , Boleslaw Szymanski , Eric Breimer

Venue:	In : 19th Annual Computer Security Applications Conferences, Las Vegas, Nevada
Citations:	40 - 8 self

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@INPROCEEDINGS{Coull03intrusiondetection:,
    author = {Scott Coull and Joel Branch and Boleslaw Szymanski and Eric Breimer},
    title = {Intrusion Detection: A Bioinformatics Approach},
    booktitle = {In : 19th Annual Computer Security Applications Conferences, Las Vegas, Nevada},
    year = {2003},
    pages = {8--12}
}

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Abstract

This paper addresses the problem of detecting masquerading, a security attack in which an intruder assumes the identity of a legitimate user. Many approaches based on Hidden Markov Models and various forms of Finite State Automata have been proposed to solve this problem. The novelty of our approach results from the application of techniques used in bioinformatics for a pair-wise sequence alignment to compare the monitored session with past user behavior. Our algorithm uses a semi-global alignment and a unique scoring system to measure similarity between a sequence of commands produced by a potential intruder and the user signature, which is a sequence of commands collected from a legitimate user. We tested this algorithm on the standard intrusion data collection set. As discussed in the paper, the results of the test showed that the described algorithm yields a promising combination of intrusion detection rate and false positive rate, when compared to published intrusion detection algorithms.

Keyphrases

intrusion detection bioinformatics approach legitimate user unique scoring system user signature pair-wise sequence alignment approach result semi-global alignment intrusion detection rate various form monitored session standard intrusion data collection false positive rate described algorithm yield hidden markov model potential intruder intrusion detection algorithm many approach finite state automaton security attack promising combination past user behavior

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