Evaluating Reputation Management Schemes of Internet of Vehicles based on Evolutionary Game Theory
This work addresses the need for more realistic evaluation of security schemes in vehicular networks, though it is incremental as it builds on existing reputation management methods by improving simulation techniques.
The paper tackles the problem of evaluating reputation management schemes in the Internet of Vehicles by proposing a simulation method that incorporates dynamic and diverse attacking strategies, using evolutionary game theory to model malicious user evolution, and applies it to a scheme with multiple utility functions, showing it can depict evolving attacks and quantify protection effectiveness.
Conducting reputation management is very important for Internet of vehicles. However, most of the existing researches evaluate the effectiveness of their schemes with settled attacking behaviors in their simulation which cannot represent the scenarios in reality. In this paper, we propose to consider dynamical and diversity attacking strategies in the simulation of reputation management scheme evaluation. To that end, we apply evolutionary game theory to model the evolution process of malicious users' attacking strategies, and discuss the methodology of the evaluation simulations. We further apply our evaluation method to a reputation management scheme with multiple utility functions, and discuss the evaluation results. The results indicate that our evaluation method is able to depict the evolving process of the dynamic attacking strategies in a vehicular network, and the final state of the simulation could be used to quantify the protection effectiveness of the reputation management scheme.