Detecting Sinkhole and Selective Forwarding Attack in Wireless Sensor Networks
Umashri Karkikatti, Dr. Nalini N
Wireless Sensor Networks (WSNs) are a promising approach that are useful for variety of applications; such as monitoring safety and security of buildings and spaces; military applications; measuring traffic flows; tracking environmental pollutants; etc. Security for WSNs is a very serious and challenging task these days as they have very important personal or national security level informations in them; mainly following are the challenges faced while designing for a robust secure WSNs; the devices in the sensor networks have severe constraints such as minimal energy; minimal computational and communicational capabilities. And secondly; there is an additional risk of physical attacks such as node capture and tampering; eavesdropping etc. Hence we need a technique which can detect the intrusion of any malicious node in the networks; which can create an alarm for taking appropriate steps to secure the information in the WSN. these techniques should be lightweight because of resource-constrained nature of WSNs. As we are aware of the different kinds of attacks for WSNs. In this paper we propose the lightweight robust technique called Received Signal Strength Indicator (RSSI) for detecting Sinkhole attacks in the WSNs. We have built our own protocol and the RSSI techniques are applied to detect the sinkhole attack. The RSSI technique doesnt cause communication overhead because it will not load the ordinary nodes since the presence of EM nodes. Also we propose a lightweight scheme called Traffic Monitor Based Selective Forwarding Attack Detection Scheme. Our approach uses EM nodes to eavesdrop and monitor all traffics of the network. RSSI technique was earlier implemented using visual sense; In this paper we have implemented RSSI technique in NS2 simulator. The simulation results show the efficient detection of the Sinkhole attacks in WSNs.
Keywords: WSN, RSSI, detecting sinkhole attacks and selective forwarding attack, intruder detection
Edition: Volume 3 Issue 6, June 2014
Pages: 1045 - 1051
Direct URL: https://www.ijsr.net/archive/v3i6/MDIwMTQ0MzE=.pdf