A Hybrid FCM Clustering- Neural Network Model for Intrusion Detection

Abstract:

Security has become an important issue for networks. Intrusion detection technology is an effective approach in dealing with the problems of network security. In this paper, we present an intrusion detection model based on hybrid fuzzy logic and neural network. The key idea is to take advantage of different classification abilities of fuzzy clustering and neural network for intrusion detection system. The new model has ability to recognize an attack, to differentiate one attack from another (i.e. classifying attacks), and the most important, to detect new attacks with high detection rate and low false negative. Training and testing data were obtained from the Defense Advanced Research Projects Agency intrusion detection evaluation data set.

• Periodical: Advanced Materials Research (Volumes 403-408)
• Pages: 3519-3527
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.403-408.3519
• Online since: November 2011
• Authors: Muna M. T. Jawhar, Monica Mehrotra
• Keywords: Component, FCM Clustering, Intrusion Detection, Neural Network (NN)

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