Learning classifiers for misuse and anomaly detection using a bag of system calls representation

Dae Ki Kang; Doug Fuller; Vasant Honavar

doi:10.1109/IAW.2005.1495942

Learning classifiers for misuse and anomaly detection using a bag of system calls representation

Dae Ki Kang, Doug Fuller, Vasant Honavar

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

118 Scopus citations

Abstract

In this paper, we propose a "bag of system calls" representation for intrusion detection in system call sequences and describe misuse and anomaly detection results with standard machine learning techniques on University of New Mexico (UNM) and MIT Lincoln Lab (MIT LL) system call sequences with the proposed representation. With the feature representation as input, we compare the performance of several machine learning techniques for misuse detection and show experimental results on anomaly detection. The results show that standard machine learning and clustering techniques on simple "bag of system calls" representation of system call sequences is effective and often performs better than those approaches that use foreign contiguous subsequences in detecting intrusive behaviors of compromised processes.

Original language	English (US)
Title of host publication	Proceedings from the Sixth Annual IEEE System, Man and Cybernetics Information Assurance Workshop, SMC 2005
Pages	118-125
Number of pages	8
DOIs	https://doi.org/10.1109/IAW.2005.1495942
State	Published - 2005
Event	6th Annual IEEE System, Man and Cybernetics Information Assurance Workshop, SMC 2005 - West Point, NY, United States Duration: Jun 15 2005 → Jun 17 2005

Publication series

Name	Proceedings from the 6th Annual IEEE System, Man and Cybernetics Information Assurance Workshop, SMC 2005
Volume	2005

Other

Other	6th Annual IEEE System, Man and Cybernetics Information Assurance Workshop, SMC 2005
Country/Territory	United States
City	West Point, NY
Period	6/15/05 → 6/17/05

All Science Journal Classification (ASJC) codes

General Engineering

Access to Document

10.1109/IAW.2005.1495942

Cite this

Kang, D. K., Fuller, D., & Honavar, V. (2005). Learning classifiers for misuse and anomaly detection using a bag of system calls representation. In Proceedings from the Sixth Annual IEEE System, Man and Cybernetics Information Assurance Workshop, SMC 2005 (pp. 118-125). Article 1495942 (Proceedings from the 6th Annual IEEE System, Man and Cybernetics Information Assurance Workshop, SMC 2005; Vol. 2005). https://doi.org/10.1109/IAW.2005.1495942

Kang, Dae Ki ; Fuller, Doug ; Honavar, Vasant. / Learning classifiers for misuse and anomaly detection using a bag of system calls representation. Proceedings from the Sixth Annual IEEE System, Man and Cybernetics Information Assurance Workshop, SMC 2005. 2005. pp. 118-125 (Proceedings from the 6th Annual IEEE System, Man and Cybernetics Information Assurance Workshop, SMC 2005).

@inproceedings{2c4c5c42cfef4dcbb0f268819914a44d,

title = "Learning classifiers for misuse and anomaly detection using a bag of system calls representation",

abstract = "In this paper, we propose a {"}bag of system calls{"} representation for intrusion detection in system call sequences and describe misuse and anomaly detection results with standard machine learning techniques on University of New Mexico (UNM) and MIT Lincoln Lab (MIT LL) system call sequences with the proposed representation. With the feature representation as input, we compare the performance of several machine learning techniques for misuse detection and show experimental results on anomaly detection. The results show that standard machine learning and clustering techniques on simple {"}bag of system calls{"} representation of system call sequences is effective and often performs better than those approaches that use foreign contiguous subsequences in detecting intrusive behaviors of compromised processes.",

author = "Kang, {Dae Ki} and Doug Fuller and Vasant Honavar",

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language = "English (US)",

isbn = "0780392906",

series = "Proceedings from the 6th Annual IEEE System, Man and Cybernetics Information Assurance Workshop, SMC 2005",

pages = "118--125",

booktitle = "Proceedings from the Sixth Annual IEEE System, Man and Cybernetics Information Assurance Workshop, SMC 2005",

note = "6th Annual IEEE System, Man and Cybernetics Information Assurance Workshop, SMC 2005 ; Conference date: 15-06-2005 Through 17-06-2005",

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Kang, DK, Fuller, D & Honavar, V 2005, Learning classifiers for misuse and anomaly detection using a bag of system calls representation. in Proceedings from the Sixth Annual IEEE System, Man and Cybernetics Information Assurance Workshop, SMC 2005., 1495942, Proceedings from the 6th Annual IEEE System, Man and Cybernetics Information Assurance Workshop, SMC 2005, vol. 2005, pp. 118-125, 6th Annual IEEE System, Man and Cybernetics Information Assurance Workshop, SMC 2005, West Point, NY, United States, 6/15/05. https://doi.org/10.1109/IAW.2005.1495942

Learning classifiers for misuse and anomaly detection using a bag of system calls representation. / Kang, Dae Ki; Fuller, Doug; Honavar, Vasant.
Proceedings from the Sixth Annual IEEE System, Man and Cybernetics Information Assurance Workshop, SMC 2005. 2005. p. 118-125 1495942 (Proceedings from the 6th Annual IEEE System, Man and Cybernetics Information Assurance Workshop, SMC 2005; Vol. 2005).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - In this paper, we propose a "bag of system calls" representation for intrusion detection in system call sequences and describe misuse and anomaly detection results with standard machine learning techniques on University of New Mexico (UNM) and MIT Lincoln Lab (MIT LL) system call sequences with the proposed representation. With the feature representation as input, we compare the performance of several machine learning techniques for misuse detection and show experimental results on anomaly detection. The results show that standard machine learning and clustering techniques on simple "bag of system calls" representation of system call sequences is effective and often performs better than those approaches that use foreign contiguous subsequences in detecting intrusive behaviors of compromised processes.

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Kang DK, Fuller D, Honavar V. Learning classifiers for misuse and anomaly detection using a bag of system calls representation. In Proceedings from the Sixth Annual IEEE System, Man and Cybernetics Information Assurance Workshop, SMC 2005. 2005. p. 118-125. 1495942. (Proceedings from the 6th Annual IEEE System, Man and Cybernetics Information Assurance Workshop, SMC 2005). doi: 10.1109/IAW.2005.1495942

Learning classifiers for misuse and anomaly detection using a bag of system calls representation

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