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Security and Communication Networks
Volume 2017, Article ID 9863032, 10 pages
https://doi.org/10.1155/2017/9863032
Research Article

GA-DoSLD: Genetic Algorithm Based Denial-of-Sleep Attack Detection in WSN

1Department of Computer Science and Engineering, NPR College of Engineering and Technology, Tamil Nadu 624001, India
2Department of Information Technology, Kamaraj College of Engineering & Technology, Tamil Nadu, India

Correspondence should be addressed to Mahalakshmi Gunasekaran; moc.liamtoh@51tiimhskalaham

Received 23 July 2016; Accepted 17 November 2016; Published 17 January 2017

Academic Editor: Qing Yang

Copyright © 2017 Mahalakshmi Gunasekaran and Subathra Periakaruppan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Denial-of-sleep (DoSL) attack is a special category of denial-of-service attack that prevents the battery powered sensor nodes from going into the sleep mode, thus affecting the network performance. The existing schemes used for the DoSL attack detection do not provide an optimal energy conservation and key pairing operation. Hence, in this paper, an efficient Genetic Algorithm (GA) based denial-of-sleep attack detection (GA-DoSLD) algorithm is suggested for analyzing the misbehaviors of the nodes. The suggested algorithm implements a Modified-RSA (MRSA) algorithm in the base station (BS) for generating and distributing the key pair among the sensor nodes. Before sending/receiving the packets, the sensor nodes determine the optimal route using Ad Hoc On-Demand Distance Vector Routing (AODV) protocol and then ensure the trustworthiness of the relay node using the fitness calculation. The crossover and mutation operations detect and analyze the methods that the attackers use for implementing the attack. On determining an attacker node, the BS broadcasts the blocked information to all the other sensor nodes in the network. Simulation results prove that the suggested algorithm is optimal compared to the existing algorithms such as X-MAC, ZKP, and TE2P schemes.