Detection and classification of anomaly intrusion using hierarchy clustering and SVM

Tang, Chenghua, Xiang, Yang, Wang, Yu, Qian, Junyan and Qiang, Baohua 2016, Detection and classification of anomaly intrusion using hierarchy clustering and SVM, Security and communication networks, vol. 9, no. 16, pp. 3401-3411, doi: 10.1002/sec.1547.

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Title Detection and classification of anomaly intrusion using hierarchy clustering and SVM
Author(s) Tang, Chenghua
Xiang, YangORCID iD for Xiang, Yang
Wang, YuORCID iD for Wang, Yu
Qian, Junyan
Qiang, Baohua
Journal name Security and communication networks
Volume number 9
Issue number 16
Start page 3401
End page 3411
Total pages 11
Publisher John Wiley & Sons
Place of publication Hoboken, N.J.
Publication date 2016-11-10
ISSN 1939-0114
Keyword(s) anomaly intrusion detection
fuzzy c-means clustering
membership function
support vector machine
Science & Technology
Computer Science, Information Systems
Computer Science
Summary Anomaly detection as a kind of intrusion detection is good at detecting the unknown attacks or new attacks, and it has attracted much attention during recent years. In this paper, a new hierarchy anomaly intrusion detection model that combines the fuzzy c-means (FCM) based on genetic algorithm and SVM is proposed. During the process of detecting intrusion, the membership function and the fuzzy interval are applied to it, and the process is extended to soft classification from the previous hard classification. Then a fuzzy error correction sub interval is introduced, so when the detection result of a data instance belongs to this range, the data will be re-detected in order to improve the effectiveness of intrusion detection. Experimental results show that the proposed model can effectively detect the vast majority of network attack types, which provides a feasible solution for solving the problems of false alarm rate and detection rate in anomaly intrusion detection model.
Language eng
DOI 10.1002/sec.1547
Field of Research 080303 Computer System Security
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, John Wiley & Sons
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Created: Mon, 22 Aug 2016, 10:26:02 EST

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