These capabilities have multiplied due to the increasing number of electronic control units (ECUs) and higher computing power. In addition, vehicle support features such as advanced driver assistance systems (ADAS), reduce driving stress and make driving safer. Vehicle connectivity provides real-time information and a variety of entertainment options. The increasingly diverse features in today’s vehicles offer drivers and passengers a more relaxed driving experience and greater convenience. Moreover, this work shows that the system has high reversion accuracy and outperforms existing systems in boundary delineation and filtering relevant messages in actual vehicles. The framework builds a multiple linear regression model for vehicle behavior and CAN traffic, filters the candidate messages based on the decision coefficients, and finally locates the bits describing the vehicle behavior to obtain the data length and alignment based on the model parameters. In this study, we propose multiple linear regression-based frameworks for bit-level inversion of CAN messages that can approximate the inversion of DBC files. However, the results of these algorithms yield only a fraction of the information specified in the DBC file regarding CAN messages, such as field boundaries and message IDs associated with specific functions. Current research reverses CAN messages through tokenization, machine learning, and diagnostic information matching to obtain details of CAN messages. This policy is ineffective against cyberattacks but limits in-depth investigation of CAN messages and hinders the development of in-vehicle intrusion detection systems (IDS) and CAN fuzz testing. To close the security holes of CAN, original equipment manufacturers (OEMs) keep the Database CAN (DBC) file describing the content of CAN messages, confidential. Numerous attacks on vehicles have been reported, and the commonality among these attacks is that they inject malicious messages into the CAN network. Although many ECUs provide convenience to drivers and passengers, they also increase the potential for cyber security threats in motor vehicles. These electronic devices form an in-vehicle network via the Controller Area Network (CAN) bus, the de facto standard for modern vehicles. Modern intelligent and networked vehicles are increasingly equipped with electronic control units (ECUs) with increased computing power.
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