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Intelligent fuzzy logic based intrusion detection system for effective detection of black hole attack in WSN

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Abstract

A wireless sensor network (WSN) is a distributed collection of tiny, low-power, wireless devices which are deployed in a physical environment to monitor the various environmental conditions. The data collected by the positioned sensor nodes is transmitted through the destination nodes by using multi hop communications. WSNs offer numerous advantages over the othernetworks, including enhanced flexibility, low cost, and simplified deployment. Due to the resource- constraint nature of WSN, it faces various challenges and issues that need to be addressed in order to ensure reliable and secure data transmission. The nodes of WSN are highly vulnerable to various types of security attacks namely black hole attack, Denial of Service (DoS), and node compromise attack. Among these attacks, black hole attack causes a serious threat to the nodes in the network. This attack is carried out by malicious nodes that intentionally drop all data packets and control packets without forwarding them to the intended destination. To ensure the security of the network for black hole attack, it is necessary to design an efficient Intrusion Detection Technique for detecting malicious nodes. In this work, a novel Fuzzy Logic-based Intrusion Detection System with Hidden Markov Model (FIDS-HMM) is proposed to identify the malicious nodes and mitigate the black hole attack. Moreover, an HMM is employed in the proposed protocol to monitor the energy levels of the nodes in order to detect the malicious nodes effectively. The implementation of the proposed protocol is carried out by using NS2 simulator. Simulation results justify the proposed protocol namely FIDS-HMM provides an efficient detection mechanism for black hole attacks in the network. Moreover, the proposed protocol improves the Quality of Service (QoS) parameters, namely packet delivery ratio, delay, and throughput in the network with efficiency.

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Authors and Affiliations

  1. Department of Information Science and Technology, College of Engineering Guindy, Anna University, Chennai, 600025, Tamil Nadu, India

    Binthiya A & Selvi Ravindran

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  1. Binthiya A
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  2. Selvi Ravindran
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Binthiya A designed the algorithm, performed the simulation results and drafted the manuscript under the supervision of Dr. Selvi Ravindran. All authors read and approved the final manuscript.

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Correspondence to Binthiya A.

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A, B., Ravindran, S. Intelligent fuzzy logic based intrusion detection system for effective detection of black hole attack in WSN. Peer-to-Peer Netw. Appl. (2024). https://doi.org/10.1007/s12083-024-01629-7

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