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Proceedings Paper

A hybrid approach for intrusion-detection based on fuzzy GNP and probabilistic classification
Author(s): S. B. Shinde; V. P. Kshirsagar; M. K. Deshmukh
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Paper Abstract

An intrusion detection system (IDS) inspects all inbound and outbound network activity and identifies suspicious patterns that may indicate a network or system attack from someone attempting to break into or compromise a system. Intrusion detection systems, which can effectively detect intrusion accesses, have attracted attention. Our work describes a novel fuzzy genetic network programming (GNP) and probabilistic classification for detecting network intrusions.Proposed method can be flexibly applied to both misuse and anomaly detection in network-intrusion-detection problems.Examples and experimental results using intrusion detection datasets DARPA99 from MIT Lincoln Laboratory demonstrate the potential of the approach.

Paper Details

Date Published: 14 March 2013
PDF: 6 pages
Proc. SPIE 8768, International Conference on Graphic and Image Processing (ICGIP 2012), 87681Y (14 March 2013); doi: 10.1117/12.2010855
Show Author Affiliations
S. B. Shinde, Government College of Engineering (India)
V. P. Kshirsagar, Government College of Engineering (India)
M. K. Deshmukh, Government College of Engineering (India)

Published in SPIE Proceedings Vol. 8768:
International Conference on Graphic and Image Processing (ICGIP 2012)
Zeng Zhu, Editor(s)

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