Hybrid Trust Based Reputation Mechanism for Discovering Malevolent Node in MANET

Abstract

A self-contained connection of wireless links that functions without any infrastructure is known as Mobile Ad Hoc Network (MANET). A MANET's nodes could engage actively and dynamically with one another. However, MAN-ETs, from the other side, are exposed to severe potential threats that are difficult to counter with present security methods. As a result, several safe communication protocols designed to enhance the secure interaction among MANET nodes. In this research, we offer a reputed optimal routing value among network nodes, secure computations, and misbehavior detection predicated on node's trust levels with a Hybrid Trust based Reputation Mechanism (HTRM). In addition, the study designs a robust Public Key Infrastructure (PKI) system using the suggested trust evaluation method in terms of "key" generation, which is a crucial component of a PKI cryptosystem. We also concentrate on the solid node authenticating process that relies on pre-authentication. To ensure edge-to-edge security, we assess safe, trustworthy routes to secure computations and authenticate mobile nodes, incorporating uncertainty into the trust management solution. When compared to other protocols, our recommended approach performs better. Finally, we use simulations data and performance evaluation metrics to verify our suggested approach's validity Our approach outperformed the competing systems in terms of overall end-to-end delay, packet delivery ratio, performance, power consumption, and key-computing time by 3.47%, 3.152%, 2.169%, and 3.527%, 3.762%, significantly.

Collaborative Attackers Detection and Route Optimization by Swarm Intelligent-based Q-learning in MANETs

“…Table 3 presents the simulation parameters in this study. The existing algorithms such as ETRS [17], HTRM [20], and DNLPPR-WMNDI [23] are also simulated using these parameters to compare their performance with the proposed algorithm. The evaluation metrics include PDR, PLR, energy consumption, throughput, and E2E delay.…”

“…However, it had low PDR and high PLR when the number of nodes was increased. In [20], the HTRM was developed to find the besttrusted path and detect misbehaving nodes based on trust values. However, the throughput and energy consumption values were not effective in detecting collaborative attacks.…”

Improved Quantum-inspired Hybrid Particle Swarm Optimization Based Resource Allocation for Quantum- Enabled IoT Devices in Ad Hoc Networks

“…Furthermore, the use of the adaptive parameter tuning technique and the global best selection strategy helps to avoid premature convergence, and ensures that the algorithm continues to explore the search space for better solutions [32]. By dynamically adjusting the parameters of the algorithm based on the performance of the swarm, the algorithm is able to adapt to different types of optimization problems and search spaces, and achieve better results.…”