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Title: Classification Trees as a Technique for Creating Anomaly-Based Intrusion Detection Systems
Authors: Jecheva, Veselina
Nikolova, Evgeniya
Keywords: Intrusion Detection
Data Mining
String Metrics
Similarity Coefficients
Issue Date: 2009
Publisher: Institute of Mathematics and Informatics Bulgarian Academy of Sciences
Citation: Serdica Journal of Computing, Vol. 3, No 4, (2009), 335p-358p
Abstract: Intrusion detection is a critical component of security information systems. The intrusion detection process attempts to detect malicious attacks by examining various data collected during processes on the protected system. This paper examines the anomaly-based intrusion detection based on sequences of system calls. The point is to construct a model that describes normal or acceptable system activity using the classification trees approach. The created database is utilized as a basis for distinguishing the intrusive activity from the legal one using string metric algorithms. The major results of the implemented simulation experiments are presented and discussed as well.
ISSN: 1312-6555
Appears in Collections:Volume 3 Number 4

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