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Publications of Jérôme Darmont

Reference (inproceedings)

H.H. Nguyen, N. Harbi, J. Darmont, "An Efficient Fuzzy Clustering-Based Approach for Intrusion Detection", 15th East-European Conference on Advances and Databases and Information Systems (ADBIS 11), Vienna, Austria, September 2011; Research Communications, Austrian Computer Society, Vienna, Austria, 117-127.


The need to increase accuracy in detecting sophisticated cyber attacks poses a great challenge not only to the research community but also to corporations. So far, many approaches have been proposed to cope with this threat. Among them, data mining has brought on remarkable contributions to the intrusion detection problem. However, the generalization ability of data mining-based methods remains limited, and hence detecting sophisticated attacks remains a tough task. In such a context, this paper presents a novel method based on both clustering and classification for developing an efficient intrusion detection system (IDS). The key idea is to take useful information exploited from fuzzy clustering into account for the process of building an IDS. To this aim, we first present theoretical cornerstones to construct additional cluster features for an intrusion detection dataset. Then, we come up with an algorithm to generate an IDS based on such cluster features and the original input features. Finally, we experimentally prove that our method is considerably superior to several state-of-the-art methods.


Classification, fuzzy clustering, intrusion detection, cyber attack


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