Volume 2 Number 6 (Dec. 2007)
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JSW 2007 Vol.2(6): 42-52 ISSN: 1796-217X
doi: 10.4304/jsw.2.6.42-52

Learning Problem and BCJR Decoding Algorithm in Anomaly-based Intrusion Detection Systems

Veselina G. Jecheva, Evgeniya P. Nikolova
Burgas Free University, Burgas, Bulgaria

Abstract—The anomaly-based intrusion detection systems examine current system activity do find deviations from normal system activity. The present paper proposes a method for normal activity description using the Hidden Markov Models (HMM), which is tuned up using the gradient based method. The obtained model is utilized as a baseline, depicting the normal system activity. The main purpose is to distinguish the normal traces of user activity from abnormal ones using the BCJR decoding algorithm. Some results from the conducted simulation experiments are introduced as well.

Index Terms—intrusion detection, anomaly-based intrusion detection, learning problem, Hidden Markov Model, BCJR decoding algorithm

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Cite: Veselina G. Jecheva, Evgeniya P. Nikolova, " Learning Problem and BCJR Decoding Algorithm in Anomaly-based Intrusion Detection Systems," Journal of Software vol. 2, no. 6, pp. 42-52, 2007.

General Information

ISSN: 1796-217X (Online)
Frequency: Monthly (2006-2019); Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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