A dynamic trust management model for vehicular ad hoc networks

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{ASLAN:2023:vehcom,

• author = "Mehmet Aslan and Sevil Sen",
• title = "A dynamic trust management model for vehicular ad hoc networks",
• journal = "Vehicular Communications",
• volume = "41",
• pages = "100608",
• year = "2023",
• ISSN = "2214-2096",
• DOI = "doi:10.1016/j.vehcom.2023.100608",
• URL = "https://www.sciencedirect.com/science/article/pii/S2214209623000384",
• keywords = "genetic algorithms, genetic programming, Vehicular ad hoc networks, Security, Trust management, Evolutionary computation, Evolutionary dynamic optimization",
• abstract = "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 89.38percent Matthews Correlation Coefficient (MCC), 91.81percent detection rate (DR), and 1.01percent false positive rate (FPR), when approx 5percent of the network traffic is malicious. The formula obtains 87.33percent MCC, 92.01percent DR, and 4.8percent FPR when approx 40percent 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",

}

Genetic Programming entries for Mehmet Aslan Sevil Sen

Citations