Recognition of grey hole attacks in wireless sensor networks using fuzzy logic in IoT

Abstract

AbstractSince the devices in Internet of Things (IoT) are always interconnected with a stable Internet connection, they are prone to attacks. In the grey hole attack, a malicious node acts as a central controller to obtain data from all the nodes and it drops and alters the data packets as per its wish. In this way, the grey hole attack alters the core concept of the IoT, which enables different devices to communicate with each other. To prevent the grey hole attack and enable efficient communication between the IoT devices, a fuzzy concept is introduced in this article. Previous methods have not proficient in spotting uncountable kinds of grey hole attacks. The fuzzy engine identifies the suspicious activity that takes place in the network by the rules generated and identifies the malicious node and stops its function immediately. The simulation experimentation is carried out for accuracy, delay, energy consumption, packet delivery ratio and throughput, and the simulation contrasts with the proposed algorithm, analog behavioral modeling (ABM), and other previous techniques. The proposed system provides an accuracy rate of 45%, a packet delivery ratio of 78%, and reduced energy consumption of 35.6% compared to the previous ABM approach. Simulation outputs showed that the proposed fuzzy grey detection technique is a proficient scheme for detecting grey hole attacks and improving network capability.