FEM – Hybrid Machine Learning Approach for the Detection of Sybil Attacks in the Wireless Sensor Networks
V. Sujatha1, E.A. Mary Anita2

1V. Sujatha, Research Scholar, AMET University, Chennai (Tamil Nadu), India.
2E.A. Mary Anita, Professor, Department of CSE, S.A. Engineering College, Chennai (Tamil Nadu), India.

Manuscript received on 01 May 2019 | Revised Manuscript received on 15 May 2019 | Manuscript published on 30 May 2019 | PP: 1171-1179 | Volume-8 Issue-7, May 2019 | Retrieval Number: G6350058719/19©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: Wireless Sensor networks finds its application in various areas such as habitat monitoring, home automation, industrial automation, military applications and health care etc. Even though Wireless sensor networks are omnipresence, they are vulnerable to the various security threats. Sybil attacks are considered to be one of the most important attacks for which the several detection algorithms and systems were designed and implemented. But still the existing algorithms need intelligence for better accuracy of detection. Hence new technique FEM(Fuzzy Extreme machines) is proposed which works on the hybrid Fuzzy and powerful Extreme learning machines for the detection of Sybil attacks. The experiments were conducted on real time Testbeds which consist of ARM as main CPU interfaced with CC2530 Zigbee transceivers and tested in LEACH environment. Results in terms of accuracy detection obtained from the FEM approach proves to be more vital when compared with the other existing classifier algorithms.
Keyword: FEM, Fuzzy, Extreme Learning Machines, Sybil Attacks, LEACH.
Scope of the Article: Machine Learning.