Kernel PCA Based Network Intrusion Feature Extraction and Detection Using SVM

• Published in: Advances in Natural Computation pp. 89 - 94
• Main Authors: Gao, Hai-Hua, Yang, Hui-Hua, Wang, Xing-Yu
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Anomaly Detection; Applied sciences; Artificial intelligence; Computer science; control theory; systems; Exact sciences and technology; Feature Extraction; Intrusion Detection; Intrusion Detection System; Memory and file management (including protection and security); Memory organisation. Data processing; Software; Support Vector Machine; Statistical analysis; Classification; Vector support machine; Feature extraction; Intruder detector; Pattern recognition; Computer security; Pattern extraction; Principal component analysis; Kernel method; Intrusion detection systems
• ISBN: 9783540283256; 3540283250; 3540283234; 9783540283232
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11539117_15

This paper proposes a novel intrusion detection approach by applying kernel principal component analysis (KPCA) for intrusion feature extraction and followed by support vector machine (SVM) for classification. The MIT’s KDD Cup 99 dataset is used to evaluate these feature extraction methods, and classification performances achieved by SVM with PCA and KPCA feature extraction are compared with those obtained by PCR and KPCR classification methods and by SVM without application of feature extraction. The results clearly demonstrate that feature extraction can greatly reduce the dimension of input space without degrading the classifiers’ performance. Among these methods, the best performance is achieved by SVM using only four principal components extracted by KPCA.