Integrating embedded systems into next-generation vehicles is proliferating as they increase safety, efficiency, and driving comfort. These functionalities are provided by hundreds of electronic control units (ECUs) that communicate with each other using various protocols that, if not properly designed, may be vulnerable to local or remote attacks. The paper presents a vehicle-security operation center for improving automotive security (V-SOC4AS) to enhance the detection, response, and prevention of cyber-attacks in the automotive context. The goal is to monitor in real-time each subsystem of intra-vehicle communication, that is controller area network (CAN), local interconnect network (LIN), FlexRay, media oriented systems transport (MOST), and Ethernet. Therefore, to achieve this goal, security information and event management (SIEM) was used to monitor and detect malicious attacks in intra-vehicle and inter-vehicle communications: messages transmitted between vehicle ECUs; infotainment and telematics systems, which provide passengers with entertainment capabilities and information about the vehicle system; and vehicular ports, which allow vehicles to connect to diagnostic devices, upload content of various types. As a result, this allows the automation and improvement of threat detection and incident response processes. Furthermore, the V-SOC4AS allows the classification of the received message as malicious and non-malicious and acquisition of additional information about the type of attack. Thus, this reduces the detection time and provides more support for response activities. Experimental evaluation was conducted on two state-of-the-art attacks: denial of service (DoS) and fuzzing. An open-source dataset was used to simulate the vehicles. V-SOC4AS exploits security information and event management to analyze the packets sent by a vehicle using a rule-based mechanism. If the payload contains a CAN frame attack, it is notified to the SOC analysts.

Modern vehicles, nowadays, come loaded with hundreds of different sensors generating a huge amount of data. This data is shared and processed among different Electronic Control Units (ECUs) through an in-vehicle network, such as the CAN bus, to improve the driver’s experience and safety. However, the implementation of new features increases exposure to cyber-attacks. The CAN bus, which is designed to grant reliable communication, has many security weaknesses that might be exploited by an attacker. The need for highly accurate real-time intrusion detection systems (IDSs) for the automotive industry is limited to classical machine learning techniques, which are usually time-consuming and have hardware limitations. In this work, we analyze an optimized and efficient version of a network-based IDS for CAN bus attack detection based on Quantum Annealing. The models were tested on two different CAN bus datasets. The results show that the Quantum Annealing algorithm outperforms a classical classification algorithm in terms of time performance, which is important in the identification of attacks in the automotive sector, and achieves similar results for detection accuracy.

Nowadays, the size and complexity of the automotive development life-cycle increase the possibility of cyber-attacks. In this context, team developers play a primary role in managing cyber security, risk assessment, and all phases of software application development (concept phases, product development, cyber security validation, production, operations, and maintenance). Currently, only generic standards exist and they are difficult to put into operation due to the lack of the required skills and knowledge. Therefore, this paper presents a vision model based on Quantum Artificial Intelligence that supports developers' decisions to integrate concrete design methods in the automotive development life-cycle. Organizations need to develop their process for developing vehicle components that comply with the new automotive standards. We suggest the usage of existing data sources (e.g., existing taxonomies) on Quantum Artificial Intelligence algorithms to suggest the best way, or the correct steps, to follow time by time to achieve user solutions.

Due to the increasing amount of software and hardware in connected and autonomous cars, the attack surface is growing, which increases the risk of security attacks. Researchers proposed machine learning or deep learning techniques to identify threats in in-vehicle networks. However, using these techniques is not enough to support the automotive industry since new processes or techniques must be conceptualized to make automotive systems more secure. Therefore, this research work presents a methodology, Quantum-based Automotive Threat Intelligence and Countermeasures (QUANTICAR), that integrates quantum optimization for CAN bus Intrusion Detection and the National Vulnerability Database (NVD) to understand the automotive attacks. In the first phase, QUANTICAR identifies the different types of attacks and then, based on the specific attack class, extracts new knowledge using the NVD. This contributes not only to improving attack detection but also to developing an Automotive Knowledge Base that can support developers and security experts in the secure development of automotive components in compliance with ISO/SAE 21434.