Title: A congestion attack behaviour recognition method for wireless sensor networks based on a decision tree

Authors: Wen Feng; Xuefeng Ding

Addresses: Informatization Construction and Management Office, Sichuan University, Chengdu, 610065, China ' Informatization Construction and Management Office, Sichuan University, Chengdu, 610065, China

Abstract: To overcome the problems of low attack recognition rates and high congestion identification errors in traditional congestion attack behaviour judgement methods for wireless sensor networks (WSNs), a new congestion attack behaviour judgement method based on decision trees is proposed in this paper. First, the traffic is processed, a congestion attack detection model is constructed as a decision tree, and the sensor nodes with artificial labels are classified and evaluated. According to the classification results, the network congestion attack behaviour is identified according to an attack recognition threshold. The experimental results show that the average recognition rate of congestion attacks is 99.82%, the error rate of congestion identification is only 0.00284, and the packet loss rate is low, indicating that this method can effectively recognise network congestion attack behaviours.

Keywords: low attack recognition rates; high congestion identification errors; decision tree; WSNs; congestion attack; attack behaviour judgement.

DOI: 10.1504/IJSNET.2021.117487

International Journal of Sensor Networks, 2021 Vol.36 No.4, pp.236 - 242

Received: 22 Jan 2021
Accepted: 22 Jan 2021

Published online: 21 Aug 2021 *

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