Volume 6 Number 12 (Dec. 2011)
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JSW 2011 Vol.6(12): 2350-2360 ISSN: 1796-217X
doi: 10.4304/jsw.6.12.2350-2360

An Efficient Hybrid Clustering-PSO Algorithm for Anomaly Intrusion Detection

Hongying Zheng, Meiju Hou, Yu Wang
College of Computer Science and Engineering, Chongqing University, Chongqing 400044, China

Abstract—Generally speaking, in anomaly intrusion detection, modeling the normal behavior of activities performed by a user or a program is an important issue. Currently most machine-learning algorithms which are widely used to establish user’s normal behaviors need labeled data for training first, so they are computational expensive and sometimes misled by artificial data. This study proposes a PSO-based optimized clustering method IDCPSO for modeling the normal patterns of a user’s activities which combines an unsupervised clustering algorithm with the PSO technique, PSO algorithm is used to optimize the clustering results and obtain the optimal detection result. IDCPSO needs unlabeled data for training and automatically establishes clusters so as to detect intruders by labeling normal and abnormal groups. The famous KDD Cup 1999 dataset is used to evaluate the proposed system. In addition, we compare the performance of PSO optimization process with GA.

Index Terms—PSO, Unsupervised Clustering, Anomaly Intrusion Detection, Optimization


Cite: Hongying Zheng, Meiju Hou, Yu Wang, "An Efficient Hybrid Clustering-PSO Algorithm for Anomaly Intrusion Detection," Journal of Software vol. 6, no. 12, pp. 2350-2360, 2011.

General Information

ISSN: 1796-217X (Online)
Frequency:  Bimonthly (Since 2020)
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, Google Scholar, ProQuest, INSPEC(IET), ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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