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DHOA-ANFIS: A Hybrid Technique to Detect Routing Attacks in Wireless Body Area Network

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Abstract

Wireless Body Area Network (WBAN) has become a reality where patient health conditions can be monitored remotely. Lightweight small sensor devices gather the health parameters and transmit the data to a remotely located medical facility. These kinds of resource-constrained networks are prone to several routing attacks, which can disrupt the whole network. Cryptographic approaches are inefficient in fighting against routing attacks, while Machine Learning based Intrusion Detection System (IDS) can identify typical and uncommon security attacks. For this purpose, a dataset has a significant role to play. This paper focuses on building a specialized dataset and an accurate Intrusion Detection Model for WBAN. A simulation has been performed using E-HARP routing protocol to simulate four routing attacks, such as Wormhole, Blackhole, Byzantine, and Scheduling attack, in addition to regular data transmission (without attack). Deer Hunting Optimization Algorithm (DHOA) and Adaptive Neuro Fuzzy inference System (ANFIS) have been merged to model a highly accurate Intrusion Detection Model. A specialized dataset has been used to train and test the hybrid DHOA-ANFIS model. The proposed model has been evaluated and compared with similar hybrid models such as CSO-ANFIS, PSO-ANFIS, BA-ANFIS, GA-ANFIS, and ABC-ANFIS. The proposed model achieves the highest overall accuracy in experiments, demonstrating its superiority over competing models. It has obtained an overall accuracy of 97.70%, a Detection Rate of 97.03%, a Precision of 96.20%, and the lowest False Alarm Rate of 0.90%. The Proposed model also outperforms state-of-the-art related works on designing machine learning based IDS for WBAN and IoT based healthcare.

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This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Computer Applications, Maulana Abul Kalam Azad University of Technology, Kolkata, India

    Sohail Saif

  2. Department of Computer Science, Chakdaha College, Chakdaha, India

    Priya Das

  3. Department of Computer Science and Engineering, Maulana Abul Kalam Azad University of Technology, Kolkata, India

    Suparna Biswas

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  1. Sohail Saif
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  2. Priya Das
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Manuscript is written by Sohail Saif, Experiments are performed by Priya Das and All the figures are prepared by Suparna Biswas.

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Correspondence to Sohail Saif.

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Saif, S., Das, P. & Biswas, S. DHOA-ANFIS: A Hybrid Technique to Detect Routing Attacks in Wireless Body Area Network. Wireless Pers Commun 133, 453–480 (2023). https://doi.org/10.1007/s11277-023-10774-w

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