Non-Intrusive Security Assessment Methods for Future Autonomous Transportation IoV

The security of the Internet of Vehicles (IoV) has always been a concern. The constantly changing IoV data under varying traffic conditions made it unsuitable for the IoV to adopt traditional anti-attack techniques. In the absence of protections, attackers can use in-car communication as a target to compromise the safety of passengers, hence the instancy to detect the security state of the IoV. However, currently available solutions require modifications to the original hardware of the IoV and are therefore very limited in applicability. In this paper, we propose a security assessment method for IoV based on Microcontroller Unit (MCU) chip temperature, called SAMCT. Specifically, we first record the MCU chip temperatures of IoV device in different security states and analyze the relationship between them. Second, the fingerprint dataset is built using the temperature residuals. Third, to forecast the security standing of IoV devices, an integration regression model based on Self-Encoders is suggested. Lastly, in order to facilitate the effectiveness of the SAMCT, a Cloud-Edge-End framework is designed with the technology of model adaptive partitioning. Results from the experiments, which were carried out on the Raspberry Pi 4B and Stm32 hardware platforms, demonstrate that the Mean Squared Error (MSE) of the SAMCT is only 0.00104 and that the execution efficiency improvement under the Cloud-Edge-End framework is significant.
Note to Practitioners
—This paper was inspired by security concerns in Internet of Vehicles communication systems. The core of this work is to provide a novel security assessment method for IoV devices based on MCU temperature, which can detect the security status of IoV devices in real-time without modifying the original hardware. To this end, the different skills from scheme design to detection and validation are explained. One crucial part of this work is to regard the MCU temperature of the IoV device as a security reference and fully integrate the critical techniques in deep learning. In addition, the proposed scheme is universal and can be applied to various scenarios such as the autonomous driving and the industrial internet of things.