A hybrid approach for intrusion-detection based on fuzzy GNP and probabilistic classification

S. B. Shinde; V. P. Kshirsagar; M. K. Deshmukh

doi:10.1117/12.2010855

14 March 2013 A hybrid approach for intrusion-detection based on fuzzy GNP and probabilistic classification

S. B. Shinde, V. P. Kshirsagar, M. K. Deshmukh

Proceedings Volume 8768, International Conference on Graphic and Image Processing (ICGIP 2012); 87681Y (2013) https://doi.org/10.1117/12.2010855
Event: 2012 International Conference on Graphic and Image Processing, 2012, Singapore, Singapore

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.

Citation Download Citation

S. B. Shinde, V. P. Kshirsagar, and M. K. Deshmukh "A hybrid approach for intrusion-detection based on fuzzy GNP and probabilistic classification", Proc. SPIE 8768, International Conference on Graphic and Image Processing (ICGIP 2012), 87681Y (14 March 2013); https://doi.org/10.1117/12.2010855

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