A New FLAME Selection Method for Intrusion Detection (FLAME-ID)
Open Access
- 1 January 2019
- journal article
- research article
- Published by Scientific Research Publishing, Inc. in Communications and Network
- Vol. 11 (01), 11-20
- https://doi.org/10.4236/cn.2019.111002
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.Keywords
This publication has 1 reference indexed in Scilit:
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