NDLI: Ant colony optimization based network intrusion feature selection and detection

Content Provider	IEEE Xplore Digital Library
Author	Hai-Hua Gao Hui-Hua Yang Xing-Yu Wang
Copyright Year	2005
Description	Author affiliation: Sch. of Inf. Sci. & Eng., East China Univ. of Sci. & Technol., Shanghai, China (Hai-Hua Gao; Hui-Hua Yang; Xing-Yu Wang)
Abstract	This paper proposes a novel intrusion detection approach by applying ant colony optimization for feature selection and SVM for detection. The intrusion features are represented as graph-ere nodes, with the edges between them denoting the adding of the next feature. Ants traverse through the graph to add nodes until the stopping criterion is satisfied. The fisher discrimination rate is adopted as the heuristic information for ants' traversal. In order to avoid training of a large number of SVM classifier, the least square based SVM estimation is adopted. Initially, the SVM is trained based on grid search method to obtain discrimination function using the training data based on all features available. Then the feature subset produced during the ACO search process is evaluated based on their abilities to reconstruct the reference discriminative function using linear least square estimation. Finally SVM is retrained using the train data based on the obtained optimal feature subset to obtain intrusion detection model. The MIT's KDD Cup 99 dataset is used to evaluate our present method, the results clearly demonstrate that the method can be an effective way for intrusion feature selection and detection.
Sponsorship	IEEE Syst., Man and Cybernetics Tech. Comm. on Cybernetics, Hong Kong Polytechnic Univ. Hebei Univ. South China Univ. Chongqing Univ. Sun Yat-sen Univ. Harbin Inst. of Technol. and Int. Univ. in Germany
Starting Page	3871
Ending Page	3875
File Size	324097
Page Count	5
File Format	PDF
ISBN	0780390911
DOI	10.1109/ICMLC.2005.1527615
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-08-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Ant colony optimization Computer vision Intrusion detection Support vector machines Support vector machine classification Machine learning Information security Least squares approximation Learning systems Information science machine learning Network intrusion detection feature selection ant colony optimization support vector machines
Content Type	Text
Resource Type	Article

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