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
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
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