Title: Design of a ML-based trust prediction model using intelligent TrustBoxes in challenged networks

Authors: Smritikona Barai; Anindita Kundu; Parama Bhaumik

Addresses: Department of Computer Science and Engineering, Heritage Institute of Technology, Kolkata, India ' Department of Software Systems, School of Computer Science and Engineering, Vellore Institute of Technology, Tamil Nadu, India ' Department of Information Technology, Jadavpur University, Kolkata, India

Abstract: Challenged networks (CNs) contain resource-constrained nodes deployed in regions where human intervention is difficult. Opportunistic networks (OppNets) are CNs with no predefined source-to-destination paths. Due to their inherent properties, CNs and OppNets are highly susceptible to black-hole (BH) attacks, resulting in degraded packet-delivery ratio. In this work, an ML-based trust prediction model (MLTPM) is proposed to identify potential BH nodes in OppNets. MLTPM uses a novel function to calculate the total-trust-value (TTV) of each node. Intelligent TrustBoxes are introduced in the network to identify possible BH nodes, using TTV, along with five more node-behaviour features. TrustBoxes reduce the computational overhead of the resource-constrained nodes. Three simulated scenarios are compared - no detection, non-ML-based detection, and MLTPM, each using epidemic, prophet, and spray-and-wait routing protocols. MLTPM performs best with spray-and-wait, exhibiting about 25.21% and 80% mean improvement in delivery-ratio and dropped-message numbers respectively, compared to non-ML-based detection. An overall 12.62% improvement in delivery-ratio and 26.7% improvement in dropped messages is observed using MLTPM, compared to the above-mentioned scenarios.

Keywords: challenged networks; opportunistic networks; trust; black-hole attack; trust-based protocols; machine-learning; security; wireless communications.

DOI: 10.1504/IJSCC.2024.141384

International Journal of Systems, Control and Communications, 2024 Vol.15 No.3, pp.209 - 234

Received: 19 Dec 2023
Accepted: 18 Mar 2024
Published online: 10 Sep 2024 *

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