A novel SVM by combining kernel principal component analysis and improved chaotic particle swarm optimization for intrusion detection

DOI10.1007/S00500-014-1332-7zbMath1382.68196OpenAlexW2050699972MaRDI QIDQ1708736

Fangjun Kuang, Siyang Zhang, Weihong Xu, Zhong Jin

Publication date: 26 March 2018

Published in: Soft Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00500-014-1332-7

zbMATH Keywords

kernel principal component analysissupport vector machineintrusion detectionchaotic particle swarm optimization

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20) Internet topics (68M11)

Related Items (1)

Parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function

Uses Software

• LIBSVM

Cites Work

• A triangle area based nearest neighbors approach to intrusion detection
• Existence of 2–Nodal Solutions for Semilinear Elliptic Equations in Unbounded Domains

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