DocumentCode
2416359
Title
An Application of Learning Problem in Anomaly-based Intrusion Detection Systems
Author
Jecheva, Veselina G. ; Nikolova, Evgeniya P.
Author_Institution
Burgas Free Univ.
fYear
2007
fDate
10-13 April 2007
Firstpage
853
Lastpage
860
Abstract
The present paper introduces an approach to anomaly-based intrusion detection using the hidden Markov models (HMM) and the BCJR decoding algorithm. The main idea is to distinguish the normal traces of user activity from abnormal ones using the BCJR decoding algorithm applied in conjunction with HMM parameters adjustment using the gradient based method. Some results from the conducted simulation experiments are introduced as well
Keywords
gradient methods; hidden Markov models; learning (artificial intelligence); security of data; BCJR decoding algorithm; anomaly-based intrusion detection systems; gradient based method; hidden Markov models; learning; user activity; Automata; Databases; Decoding; Hidden Markov models; Intrusion detection; Pattern matching; Pattern recognition; Protection; Sequences; Specification languages;
fLanguage
English
Publisher
ieee
Conference_Titel
Availability, Reliability and Security, 2007. ARES 2007. The Second International Conference on
Conference_Location
Vienna
Print_ISBN
0-7695-2775-2
Type
conf
DOI
10.1109/ARES.2007.35
Filename
4159884
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