| Title | Connected and Autonomous Vehicles against a Malware Spread : A Stochastic Modeling Approach |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | El Mouhib, Manal, Azghiou, Kamal, Benali, Abdelhamid |
| Conference Name | 2022 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS) |
| Keywords | Adaptation models, Autonomous and Connected Vehicle, CAV, Compartmental model, connected vehicles, intelligent transportation systems, Malware, pubcrawl, Resiliency, Scalability, security, Stochastic Computing Security, stochastic model, Stochastic processes, Transportation, visualization |
| Abstract | The proliferation of autonomous and connected vehicles on our roads is increasingly felt. However, the problems related to the optimization of the energy consumed, to the safety, and to the security of these do not cease to arise on the tables of debates bringing together the various stakeholders. By focusing on the security aspect of such systems, we can realize that there is a family of problems that must be investigated as soon as possible. In particular, those that may manifest as the system expands. Therefore, this work aims to model and simulate the behavior of a system of autonomous and connected vehicles in the face of a malware invasion. In order to achieve the set objective, we propose a model to our system which is inspired by those used in epidimology, such as SI, SIR, SIER, etc. This being adapted to our case study, stochastic processes are defined in order to characterize its dynamics. After having fixed the values of the various parameters, as well as those of the initial conditions, we run 100 simulations of our system. After which we visualize the results got, we analyze them, and we give some interpretations. We end by outlining the lessons and recommendations drawn from the results. |
| DOI | 10.1109/IEMTRONICS55184.2022.9795713 |
| Citation Key | el_mouhib_connected_2022 |
Connected and Autonomous Vehicles against a Malware Spread : A Stochastic Modeling Approach