Wormhole Attacks Detection in Wireless Sensor Networks by Analyzing Transmission range and Residual Energy of Nodes

Abstract

Wireless Sensor Networks have made significant progress and have emerged as an important study topic in wireless and distributed networks. Wireless sensor networks (WSN) are made up of a large number of tiny and inexpensive devices called sensor nodes. The sensor nodes are capable of detecting, actuating, and regulating the information gathered. There are several research challenges and issues with WSN such as security, power efficiency, scalability, responsiveness, and reliability. security becomes a key prerequisite for modern-age applications. Weak security or absence of security may not only conciliate classified information but also makes them accessible for malicious attacks. The network layer is vulnerable to different types of attacks like a Sinkhole, Wormhole, Sybil, Selective Forwarding, Hello Flood, Black Hole, greyhole, and so on. This paper deals with the detection and prevention of an attack on the network layer called a wormhole attack. A wormhole attack is one of the most popular and serious attacks in the wireless sensor network. It is a particularly damaging attack on routing protocols for specially designated systems in which two or more collaborating attackers record packets at one location and tunnel them to another for replay at that remote location. In This paper, we make a literature review of the detection and prevention of wormhole attacks. Also proposed transmission range-based and residual energy mechanisms for the detection of wormhole attacks. When the source node received RREP, it tracks the location of nodes on the route using GPS and records the actual distance between them and the minimum number of hops. Simulation results are used with the NS-2 simulator and our method has been evaluated in terms of detection rate, packet delivery ratio, throughput, and residual energy compare to a network without or with an attack. And results show that the detection rate of our method is 89.5% of the total adversary attacks conducted.