Detecting Intrusion Behavior in Communication Networks using Firefly-based Fuzzy Clustering Approach
D. Veeraiah1, Tejaswi Kavuru2, Ebenezer Jangam3, P. Victer Paul4
1D. Veeraiah, Department of Computer Science and Engineering, Vignan’s Foundation for Science, Technology and Research Deemed to be University, (A.P), India.
2Tejaswi Kavuru, Department of Computer Science and Engineering, Vignan’s Foundation for Science, Technology and Research Deemed to be University, (A.P), India.
3Ebenezer Jangam, Department of Computer Science and Engineering, Vignan’s Foundation for Science, Technology and Research Deemed to be University, (A.P), India.
4P. Victer Paul, Department of Computer Science and Engineering, Vignan’s Foundation for Science, Technology and Research Deemed to be University, (A.P), India.
Manuscript received on 12 February 2019 | Revised Manuscript received on 02 March 2019 | Manuscript Published on 08 June 2019 | PP: 96-99 | Volume-7 Issue-5S4, February 2019 | Retrieval Number: E10180275S419/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Intrusion detection system is responsible to identify any suspicious activity in a communication network. Researchers proposed diverse methods for intrusion detection as it is a necessary task to provide security for the communication network and users. In this paper, intru-sion detection system is proposed using the combination of Firefly algorithm and fuzzy clustering. Initially, firefly algorithm is used to optimize the separation between the clusters. The output of firefly algorithm is given to the fuzzy clustering. Fuzzy clustering is used to differentiate the malicious activity from the normal activity. The proposed approach is evaluated on benchmark IDS datasets and the results are encouraging.
Keywords: Fuzzy C-Means (FCM), Firefly Algorithm (FA) and Intrusion Detection System (IDS).
Scope of the Article: Fuzzy Logics