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As technology develops rapidly, people are usually expected to increase their life quality day by day, especially in the automotive sector. As the automotive technology develops, the number of units, electronic control units (ECU) that fulfil the wishes of the people in the vehicle is increasing as day pass. The controller area network (CAN) is widely used due to the real-time performance and efficient communication of electronic control units that respond to the requests of the people in their vehicle. However, discussions on how to secure the network of CAN communication have increased recently. According to research, the control of this communication network, which is simple to control and vulnerable in nature, can be easily taken over by automotive hackers. It has been seen in the researches that the hackers who infiltrated the CAN communication network in the vehicle have effects not only on the vehicle but also on human health by remotely controlling the electronic control units. The researchers, who did not remain silent against the security gaps that emerged as automotive technology developed, covered the precautions that should be taken in their articles. The aim of this research is to defend the attacks by establishing a hacker detection unit (KBU) in the in-vehicle communication network and determining the presence of hacker and providing simple encrypted communication of electronic control units in multiple ways. The hybrid method used in this research includes both encrypted communication and an attack detection unit. As a result of the experiments, this hybrid structure of the method mentioned in this study provides both in-depth security in CAN communication and ensures that it does not compromise its canonical structure. [ABSTRACT FROM AUTHOR]

The communication and connectivity functions of vehicles increase their vulnerability to hackers. The unintended failure and malfunction of in-vehicle systems caused by external factors threaten the security and safety of passengers. As the controller area network alone cannot protect vehicles from external attacks, techniques to analyze and detect external attacks are required. Therefore, we propose a multi-labeled hierarchical classification (MLHC) intrusion detection model that analyzes and detects external attacks caused by message injection. This model quickly determines the occurrence of attacks and classifies the attack using only existing classified attack data. We evaluated the performance of the model by analyzing its learning space. We further verified the model by comparing its accuracy, F1 score and data learning and evaluation times with the two layers multi-class detection (TLMD) and single-layer multi-class classification (SLMC) models. The simulation results show that the MLHC model has the highest F1 score of 0.9995 and is 87.30% and 99.92% faster than the SLMC and TLMD models in terms of detection time, respectively. Consequently, the proposed model can classify both the type and existence or absence of attacks with high accuracy and can be used in interior communication environments of high-speed vehicles with a high throughput. [ABSTRACT FROM AUTHOR]