The Research on Fuzzy Clustering Method Based on Differential Evolution Algorithm in Intrusion Detection

Abstract:

Fuzzy C-means clustering algorithm (FCM) is widely applied to the intrusion detection. To acquire a better division for intrusion data, a new method (DEFCM) presented in the paper which combines FCM and differential evolution algorithm (DE) is found application. As a start, several randomly initiated partitions are optimized by FCM, and then the result is provided to differential evolution algorithm. After that, the combined result is sent to FCM again to adjust the partition and obtain the final answer. The method can improve detection performance effectively. The KDDCUP1999 data set is used in the simulation experiment, and the result proves that the DEFCM algorithm has a comparatively high detection rate in intrusion detection.

• Periodical: Applied Mechanics and Materials (Volumes 651-653)
• Pages: 547-550
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.651-653.547
• Online since: September 2014
• Authors: Qi Fan Yang*, Li Na Wang
• Keywords: Differential Evolution Algorithm (DE), Fuzzy C-Means (FCM), Intrusion Detection

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