Open Access

ARTICLE

A Rule-Based Approach for Grey Hole Attack Prediction in Wireless Sensor Networks

C. Gowdham^*, S. Nithyanandam

Department of Computer Science and Engineering, PRIST Deemed to be University, Thanjavur, 613403, Tamilnadu, India

* Corresponding Author: C. Gowdham. Email:

Intelligent Automation & Soft Computing 2023, 35(3), 3815-3827. https://doi.org/10.32604/iasc.2023.031876

Received 28 April 2022; Accepted 16 June 2022; Issue published 17 August 2022

Abstract

The Wireless Sensor Networks (WSN) are vulnerable to assaults due to the fact that the devices connected to them have a reliable connection to the internet. A malicious node acts as the controller and uses a grey hole attack to get the data from all of the other nodes in the network. Additionally, the nodes are discarding and modifying the data packets according to the requirements of the system. The assault modifies the fundamental concept of the WSNs, which is that different devices should communicate with one another. In the proposed system, there is a fuzzy idea offered for the purpose of preventing the grey hole attack from making effective communication among the WSN devices. The currently available model is unable to recognise the myriad of different kinds of attacks. The fuzzy engine identified suspicious actions by utilising the rules that were generated to make a prediction about the malicious node that would halt the process. Experiments conducted using simulation are used to determine delay, accuracy, energy consumption, throughput, and the ratio of packets successfully delivered. It stands in contrast to the model that was suggested, as well as the methodologies that are currently being used, and analogue behavioural modelling. In comparison to the existing method, the proposed model achieves an accuracy rate of 45 percent, a packet delivery ratio of 79 percent, and a reduction in energy usage of around 35.6 percent. These results from the simulation demonstrate that the fuzzy grey detection technique that was presented has the potential to increase the network’s capability of detecting grey hole assaults.

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Keywords

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