Title: Improved fault tolerant SPRT detection method for node replication attacks in wireless sensor networks

Authors: Hsin-Hsiu Chen; Cooper Cheng-Yuan Ku; David C. Yen

Addresses: Institute of Information Management, National Yang Ming Chiao Tung University, 1001, University Rd., Hsinchu City, Taiwan ' Institute of Information Management, National Yang Ming Chiao Tung University, 1001, University Rd., Hsinchu City, Taiwan ' JHJ School of Business, Texas Southern University, 3100 Cleburne Street, Houston, TX 77004, USA

Abstract: As the internet of things (IoT) emerges, the application and usage of the wireless sensor network (WSN) have been increasing quickly. Meanwhile, there are many threats and risks of information security that need to be effectively dealt with. One of these network attacks is the node replication attack. The attackers may catch sensor nodes, clone them, release them into the original network, and launch various internal attacks. Many replica detection instruments/methods are proposed for this type of attack. However, most detection are characterised as high computation and communication costs. Some detection methods based on the sequential probability ratio test (SPRT) indicate much lower requirements of system overhead, but these prior works may sacrifice efficiency due to frequent retransmission of the message. In this paper, we propose a fault-tolerant method for replica detection based on the SPRT in WSNs. To improve the efficiency and reliability, we use the residual energy and slope of energy consumption of the node as appendices and then apply the SPRT to adjust the detection rate dynamically. The simulation results show that our proposed scheme achieves a better performance on the efficiency of detection and reduction of error rates.

Keywords: clone attack; wireless sensor network; WSN; node replication detection; sequential probability ratio test; SPRT.

DOI: 10.1504/IJSNET.2022.10049985

International Journal of Sensor Networks, 2022 Vol.39 No.4, pp.279 - 288

Received: 04 Dec 2021
Accepted: 05 Dec 2021

Published online: 30 Aug 2022 *

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