• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

A Lightweight Detection Mechanism against Sybil Attack in Wireless Sensor Network

Vol. 9, No. 9, September 29, 2015
10.3837/tiis.2015.09.025, Download Paper (Free):

Abstract

Sybil attack is a special kind of attack which is difficult to be detected in Wireless Sensor Network (WSN). So a lightweight detection mechanism based on LEACH-RSSI-ID (LRD) is proposed in this paper. Due to the characteristic of Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol, none of nodes can be the cluster head forever. Meanwhile, in order to consume less energy, both factors which are called the remaining energy of nodes and relative density of nodes are taken into account. Therefore, Sybil attack can be found by analyzing the RSSI-ID tables. Different from the previous detection methods, even though Sybil attack occurs in the initialization phase, the malicious nodes can be detected by sink node. What。ッs more, when each malicious node frequently changes identification, it will be detected in a short time. Through the simulations, it is revealed that the LRD mechanism can detect the Sybil attack with high detection rate and accuracy.


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Cite this article

[IEEE Style]
W. Shi, S. Liu, Z. Zhang, "A Lightweight Detection Mechanism against Sybil Attack in Wireless Sensor Network," KSII Transactions on Internet and Information Systems, vol. 9, no. 9, pp. 3738-3750, 2015. DOI: 10.3837/tiis.2015.09.025.

[ACM Style]
Wei Shi, Sanyang Liu, and Zhaohui Zhang. 2015. A Lightweight Detection Mechanism against Sybil Attack in Wireless Sensor Network. KSII Transactions on Internet and Information Systems, 9, 9, (2015), 3738-3750. DOI: 10.3837/tiis.2015.09.025.

[BibTeX Style]
@article{tiis:20902, title="A Lightweight Detection Mechanism against Sybil Attack in Wireless Sensor Network", author="Wei Shi and Sanyang Liu and Zhaohui Zhang and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2015.09.025}, volume={9}, number={9}, year="2015", month={September}, pages={3738-3750}}