LS-SVM Based Intrusion Detection using Kernel Space Approximation and Kernel-Target Alignment

Gao, Haihua; Wang, Xingyu; Yang, Huihua

doi:10.1109/wcica.2006.1713169

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…In [16], Weston and Watkins propose a formulation of the SVM that enable a multi-class patter recognition problem to be solved in a single optimization. There are other efforts to be done in IDSs using SVMs [17][18] which improve the response time and decrease the ratio of false alarms. But these SVMs are off-line analysis approach due to the time used for gathering information necessary to compute features.…”

Section: Related Workmentioning

confidence: 99%

Heterogeneous Multi-Sensor Data Fusion with Multi-Class Support Vector Machines: Creating Network Security Situation Awareness

Liu

Wang

Liu

et al. 2007

2007 International Conference on Machine Learning and Cybernetics

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Multi-sensor Data Fusion and Network SituationAwareness are emerging technique in the field of network security and they help administrators to be aware of the actual security situation of their networks. This paper mainly focuses on heterogeneous multi-sensor data fusion and Situation Awareness. We adopted Snort and NetFlow collector as two sensors to gather real network traffic and fused them use Multi-class Support Vector Machines that could solve a multi class problem. In order to avoid dimension disaster, we employed an effective feature reduction approach to decrease the dimension of input vector and the computation time of Support Vector Machines that improved fusion performance and real time characteristic. Our framework is proved to be feasible and effective and has better performance than Neural Network through a series of experiments that using real network traffic.

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Section: Related Workmentioning

confidence: 99%

Heterogeneous Multi-Sensor Data Fusion with Multi-Class Support Vector Machines: Creating Network Security Situation Awareness

Liu

Wang

Liu

et al. 2007

2007 International Conference on Machine Learning and Cybernetics

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“…Reference [1] proposes kernel partial least squares method for intrusion feature extraction and detection, effectively accomplishing intrusion feature extraction and discrimination.Reference [2] applies principal component analysis (PCA) and kernel principal component analysis (KPCA) to extract intrusion features, and support vector machine (SVM) is used for intrusion detection. Reference [3] applies principal component neural network (PCNN) and SVM to the intrusion detection, which reduces the dimension of input data effectively.…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction Method for Network Intrusion Detection Based on RS-KPCA

Wang

Bai

et al. 2014

AMM

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For the complexity and nonlinearity of the input characteristics in network intrusion detection system, a feature extraction method for network intrusion detection based on RS-KPCA is studied. Firstly, the Rough Set (RS) theory is used to select the valuable features, while the unnecessary features are removed. Then, the features of the intrusion detection sample data are extracted by the kernel principal component analysis (KPCA) algorithm. The number of new features is determined by the cumulative contribution rate. Simulation results show that this method can effectively remove the interference features, and has the advantages of obvious principal component feature and concentrated contribution rate, compared with PCA. Overall, the proposed method can effectively integrate the nonlinear features of the original data, reduce the dimension, and improve the intrusion detection performance.

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The improved VQ-MAP and its combination with LS-SVM for speaker recognition

Zhan

Zhao

2013

IEEE Conference Anthology

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LS-SVM Based Intrusion Detection using Kernel Space Approximation and Kernel-Target Alignment

Cited by 5 publications

References 13 publications

Heterogeneous Multi-Sensor Data Fusion with Multi-Class Support Vector Machines: Creating Network Security Situation Awareness

Heterogeneous Multi-Sensor Data Fusion with Multi-Class Support Vector Machines: Creating Network Security Situation Awareness

Feature Extraction Method for Network Intrusion Detection Based on RS-KPCA

The improved VQ-MAP and its combination with LS-SVM for speaker recognition

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