A dynamic trust management model for vehicular ad hoc networks

Trust management in vehicular ad hoc networks (VANETs) is a challenging dynamic optimization problem due to their decentralized, infrastructureless, and dynamically changing topology. Evolutionary computation (EC) algorithms are good candidates for solving dynamic optimization problems (DOPs), since they are inspired from the biological evolution that is occurred as a result of changes in the environment. In this study, we explore the use of genetic programming (GP) algorithm and evolutionary dynamic optimization (EDO) techniques to build a dynamic trust management model for VANETs. The proposed dynamic trust management model properly evaluates the trustworthiness of vehicles and their messages in the simulation of experimental scenarios including bogus information attacks. The simulation results show that the evolved trust calculation formula prevents the propagation of bogus messages over VANETs successfully and the dynamic trust management model detects changes in the problem and reacts to them in a timely manner. The best evolved formula achieves 89.38% Matthews Correlation Coefficient (MCC), 91.81% detection rate (DR), and 1.01% false positive rate (FPR), when approximate to 5% of the network traffic is malicious. The formula obtains 87.33% MCC, 92.01% DR, and 4.8% FPR when approximate to 40% of the network traffic is malicious, demonstrating its robustness to increasing malicious messages. The proposed model is also run on a real-world traffic model and obtains high MCC and low FPR values. To the best of our knowledge, this is the first application of EC and EDO techniques that generate a trust formula automatically for dynamic trust management in VANETs. (c) 2023 Elsevier Inc. All rights reserved.

Automated summary

This study proposes a dynamic trust management model for VANETs using genetic programming algorithm and evolutionary dynamic optimization techniques. The model successfully evaluates the trustworthiness of vehicles and messages, prevents the propagation of bogus messages, and detects changes in the network in a timely manner. The proposed model shows robustness to increasing malicious messages and achieves high detection rate and low false positive rate.