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New vehicles have become increasingly targeted for cyber-attacks as their rate of digitalization is accelerated. Research on vehicle hacking has highlighted the security vulnerabilities of in-vehicle controller area networks (CANs) as the biggest problem. In particular, a CAN does not offer access control, authentication, or confidentiality, so it fails to prevent reconnaissance operations conducted by an adversary. Because its static configuration (CAN ID, data frame transmission cycle, and data field format) is used in an in-vehicle network environment, the adversary can conduct reconnaissance and easily acquire information to be used for an attack. One of the moving target defense strategies, network address shuffling (NAS), is an extremely practical security solution that can prevent in-vehicle CAN reconnaissance acts. In this paper, we propose a CAN ID shuffling technique using NAS. Our proposed security solution aims to increase the cost burden for the adversary to analyze CAN data frames. To evaluate the performance of the proposed security solution, we conducted an evaluation based on a labcar. Our proposed security solution may be implemented without altering the unique characteristics of the CAN standard. Hence, it can be used as a practical countermeasure to solve the problems affecting in-vehicle CANs.

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]

With the development of 5G and Internet of Vehicles technology, the possibility of remote wireless attack on an in-vehicle network has been proven by security researchers. Anomaly detection technology can effectively alleviate the security threat, as the first line of security defense. Based on this, this paper proposes a distributed anomaly detection system using hierarchical temporal memory (HTM) to enhance the security of a vehicular controller area network bus. The HTM model can predict the flow data in real time, which depends on the state of the previous learning. In addition, we improved the abnormal score mechanism to evaluate the prediction. We manually synthesized field modification and replay attack in data field. Compared with recurrent neural networks and hidden Markov model detection models, the results show that the distributed anomaly detection system based on HTM networks achieves better performance in the area under receiver operating characteristic curve score, precision, and recall.

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.

The controller area network with flexible data rate (CAN-FD) is attracting attention as the next generation of in-vehicle network technology. However, security issues have not been completely taken into account when designing CAN-FD, although every bit of information transmitted could be critical to driver safety. If we fail to solve the security vulnerabilities of CAN-FD, we cannot expect Vehicle-Information and Communications Technology (Vehicle-ICT) convergence to continue to develop. Fortunately, secure in-vehicle CAN-FD communication environments can be constructed using the larger data payload of CAN-FD. In this paper, we propose a security architecture for in-vehicle CAN-FD as a countermeasure (designed in accordance with CAN-FD specifications). We considered the characteristics of the International Organization for Standardization (ISO) 26262 Automotive Safety Integrity Level and the in-vehicle subnetwork to design a practical security architecture. We also evaluated the feasibility of the proposed security architecture using three kinds of microcontroller unit and the CANoe software. Our evaluation findings may be used as an indicator of the performance level of electronic control units for manufacturing next-generation vehicles. [ABSTRACT FROM PUBLISHER]

Vehicle-IT convergence technology is a rapidly rising paradigm of modern vehicles, in which an electronic control unit (ECU) is used to control the vehicle electrical systems, and the controller area network (CAN), an in-vehicle network, is commonly used to construct an efficient network of ECUs. Unfortunately, security issues have not been treated properly in CAN, although CAN control messages could be life-critical. With the appearance of the connected car environment, in-vehicle networks (e.g., CAN) are now connected to external networks (e.g., 3G/4G mobile networks), enabling an adversary to perform a long-range wireless attack using CAN vulnerabilities. In this paper we show that a long-range wireless attack is physically possible using a real vehicle and malicious smartphone application in a connected car environment. We also propose a security protocol for CAN as a countermeasure designed in accordance with current CAN specifications. We evaluate the feasibility of the proposed security protocol using CANoe software and a DSP-F28335 microcontroller. Our results show that the proposed security protocol is more efficient than existing security protocols with respect to authentication delay and communication load. [ABSTRACT FROM PUBLISHER]

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]