An Efficient Fuzzy Clustering-Based Approach for Intrusion Detection

Huu Hoa Nguyen (1) , Nouria Harbi (1) , Jérôme Darmont (1)

1 ERIC - Equipe de Recherche en Ingénierie des Connaissances

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.

Mots clés: classification; fuzzy clustering; intrusion detection; cyber attack

Domaines: Base de données [cs.DB]

Huu Hoa Nguyen, Nouria Harbi, Jérôme Darmont. An Efficient Fuzzy Clustering-Based Approach for Intrusion Detection. 15th East-European Conference on Advances and Databases and Information Systems (ADBIS 2011), Sep 2011, Vienna, Austria. pp.117-127. ⟨hal-00631499⟩