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Detection and classification of intrusions and faults using sequences of system calls
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Volume 30 ,  Issue 4  (December 2001) table of contents
SPECIAL ISSUE: Special section on data mining for intrusion detection and threat analysis table of contents
Pages: 25 - 34  
Year of Publication: 2001
ISSN:0163-5808
Authors
João B. D. Cabrera  Scientific Systems Company, Woburn MA
Lundy Lewis  Aprisma Management Technologies, Durham, NH
Raman K. Mehra  Scientific Systems Company, Woburn MA
Publisher
ACM  New York, NY, USA
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ABSTRACT

This paper investigates the use of sequences of system calls for classifying intrusions and faults induced by privileged processes in Unix. Classification is an essential capability for responding to an anomaly (attack or fault), since it gives the ability to associate appropriate responses to each anomaly type. Previous work using the well known dataset from the University of New Mexico (UNM) has demonstrated the usefulness of monitoring sequences of system calls for detecting anomalies induced by processes corresponding to several Unix Programs, such as sendmail, lpr, ftp, etc. Specifically, previous work has shown that the Anomaly Count of a running process, i.e., the number of sequences spawned by the process which are not found in the corresponding dictionary of normal activity for the Program, is a valuable feature for anomaly detection. To achieve Classification, in this paper we introduce the concept of Anomaly Dictionaries, which are the sets of anomalous sequences for each type of anomaly. It is verified that Anomaly Dictionaries for the UNM's sendmail Program have very little overlap, and can be effectively used for Anomaly Classification. The sequences in the Anomalous Dictionary enable a description of Self for the Anomalies, analogous to the definition of Self for Privileged Programs given by the Normal Dictionaries. The dependence of Classification Accuracy with sequence length is also discussed. As a side result, it is also shown that a hybrid scheme, combining the proposed classification strategy with the original Anomaly Counts can lead to a substantial improvement in the overall detection rates for the sendmail dataset. The methodology proposed is rather general, and can be applied to any situation where sequences of symbols provide an effective characterization of a phenomenon.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
João B. D. Cabrera: colleagues
Lundy Lewis: colleagues
Raman K. Mehra: colleagues