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Ganapathy, S., Yogesh, P. and Kannan, A. (2012) Intelligent Agent Based Intrusion Detection Using Enhanced Multiclass SVM. Computational Intelligence and Neuroscience, 10.

has been cited by the following article:

  • TITLE: A New FLAME Selection Method for Intrusion Detection (FLAME-ID)

    AUTHORS: Wafa Alsharafat

    KEYWORDS: FLAME, Intrusion Detection, XCS, Genetic Algorithm

    JOURNAL NAME: Communications and Network, Vol.11 No.1, January 28, 2019

    ABSTRACT: Due to the ever growing number of cyber attacks, especially of the online systems, development and operation of adaptive Intrusion Detection Systems (IDSs) is badly needed so as to protect these systems. It remains as a goal of paramount importance to achieve and a serious challenge to address. Different selection methods have been developed and implemented in Genetic Algorithms (GAs) to enhance the rate of detection of the IDSs. In this respect, the present study employed the eXtended Classifier System (XCS) for detection of intrusions by matching the incoming environmental message (packet) with a classifiers pool to determine whether the incoming message is a normal request or an intrusion. Fuzzy Clustering by Local Approximation Membership (FLAME) represents the new selection method used in GAs. In this study, Genetic Algorithm with FLAME selection (FGA) was used as a production engine for the XCS. For comparison purposes, different selection methods were compared with FLAME selection and all experiments and evaluations were performed by using the KDD’99 dataset.