Energy trust system for detecting sybil attack in clustered wireless sensor networks

Abstract

Wireless Sensor Network (WSN) is an emerging technology that offers great promise for various applications. The sensing capabilities combined with relatively small processing power and wireless communication makes it one of the main technologies to be exploited in the future. Despite its attractive features, WSN is vulnerable to various security attacks. The constraints of WSN such as limited energy and memory make the security problem even more critical. One of the security issues of WSN is it is susceptible to sybil attack. In this attack, the adversary forges multiple entities to disrupt the entire network. This paper addresses the problem by developing a lightweight trust system using energy as a metric parameter for a hierarchical WSN. The performance evaluation of this system shows efficiency and scalability for detecting sybil attacks in terms of true and false positive detection in a heterogeneous WSN. Furthermore, this system reduces the communication overhead in the network by cancelling feedback and recommendations among sensor nodes (SNs).

A lightweight key renewal scheme based authentication protocol with configurable RO PUF for clustered sensor networks

“…Attackers can also impersonate network nodes by counterfeiting, and conceal legitimate users to execute unauthorized operations. The active attacks include jamming attack [15], denial of service attack, black hole attack [16], tampering attack, wormhole attack [17], selective forwarding, identity replication/cloning attack [18], Sybil attack [19], etc. The passive attack is difficult to detect for the simple reason that the attacker only monitors the transmitted data without modifying the message.…”

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“…Noor Alsaedi, et.al presented the issues being faced when a lightweight trust system is generated. In order to address this issue for hierarchical WSN, a metric parameter that utilized energy is proposed [8]. It is seen through the performance evaluations that for the detection of Sybil attacks with respect to true and false positive identifications, the results achieved as highly efficient and scalable.…”

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“…In addition attacks that are common on dataset are also elaborated through this literature. [3]The attacks on dataset could be on resources, network or data. The attacks on resources causes denial of service and hence extra time is consumed while accessing resources.…”