Open Access Open Access  Restricted Access Subscription or Fee Access

Sparse Least Squares Support Vector Clustering Algorithm for Anomaly Detection

Hao Xiong, Sheng Sun


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.


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

Full Text:



  • There are currently no refbacks.

Disclaimer/Regarding indexing issue:

We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.