Genetic-fuzzy rule mining approach and evaluation of feature selection techniques for anomaly intrusion detection

DOI10.1016/J.PATCOG.2006.12.009zbMath1119.68189OpenAlexW2114834796MaRDI QIDQ885172

Hanli Wang, Sam Kwong, Chi-Ho Tsang

Publication date: 8 June 2007

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2006.12.009

zbMATH Keywords

multi-objective optimizationgenetic algorithmsfeature selectionintrusion detectionfuzzy classifier

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items (5)

Solving multi-class problems with linguistic fuzzy rule based classification systems based on pairwise learning and preference relations ⋮ A Compact Belief Rule-Based Classification System with Evidential Clustering ⋮ Semi-overlap functions and novel fuzzy reasoning algorithms with applications ⋮ A fast and efficient multi-objective evolutionary learning scheme for fuzzy rule-based classifiers ⋮ Belief rule-based classification system: extension of FRBCS in belief functions framework

Uses Software

• UCI-ml

Cites Work

• Multi-objective hierarchical genetic algorithm for interpretable fuzzy rule-based knowledge extraction
• Ten years of genetic fuzzy systems: Current framework and new trends.
• A note on genetic algorithms for large-scale feature selection
• Outline of a New Approach to the Analysis of Complex Systems and Decision Processes
• Mathematical Derivation of an Election System
• Adaptive intrusion detection: A data mining approach
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