An Efficient Fuzzy Clustering-Based Approach for Intrusion Detection

CEUR Workshop Proceedings 10/2011; 789.
Source: arXiv


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 this thread, we present 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 cornerstones to construct additional cluster features for a
training set. 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 outperforms several well-known methods.

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Available from: Jérôme Darmont, Jan 23, 2014
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