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Sparse Least Squares Support Vector Clustering Algorithm for Anomaly Detection

Hao Xiong, Sheng Sun

Abstract


Intrusion detection system has become an important part of network security technology. The major benefit of anomaly detection algorithms is their ability to potentially detect unforeseen attacks. In this paper, we present a Least Squares Support Vector Clustering (LSSVC) algorithm first. LSSVC has the advantages which can cluster mess of data and identify noise. But this algorithm has the problem that sparseness is lost. In view of its shortcomings, then we utilize a pruning method to clip support vector set, and propose a new sparse LSSVC. Recognition precision and recognition speed are improved after pruning. The experimental results show that our new algorithm has high feasibility and validity, and it will have vast potential for future development in the field of intrusion detection.

Keywords


least squares support clustering, kernel method, anomaly detection..

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