A review of research on information security risk identification and response strategies for intelligent connected vehicles

summary:

With the rapid development of intelligent connected vehicle technology, its information security issues have gradually become the focus of public attention. This paper outlines the development background of intelligent connected vehicle technology and the sources of information security risks. It uses the STRIDE threat analysis method to identify risks in the four-layer model of intelligent connected vehicles, and further explores key technologies such as anti-sybil attack strategies and certificateless public key authentication systems. Finally, the construction of an intelligent connected vehicle security evaluation system is proposed, and future research directions and potential technical solutions are discussed. This paper aims to provide a comprehensive analysis framework and response strategies for the field of information security of intelligent connected vehicles to promote the healthy development of this field.

With the rapid development of the global automotive industry, Intelligent Connected Vehicles (ICVs), as the forefront of automotive technology innovation, are gradually changing the way we travel. They integrate advanced sensors, communication modules and computing platforms, and can achieve multiple functions such as automatic driving, traffic information sharing, and remote monitoring. The integration of these technologies has not only brought unprecedented convenience to users, but also made great contributions to improving traffic safety and efficiency.

However, the rapid development of intelligent connected vehicles has also brought about a series of information security risks. The networking of vehicles makes them potential targets of cyber attacks. Hackers may illegally control vehicles through remote attack methods, steal user data, and even cause serious traffic accidents. Including but not limited to research on CAN bus anomaly detection technology, information security issues of the Internet of Vehicles, and network security research on intelligent connected vehicles. These threats mainly come from the openness and complexity of intelligent connected vehicles, such as attacks on external interfaces, security challenges brought by wireless communication technology, and design constraints and challenges brought by the characteristics of the vehicle network structure. In addition, the complex electronic systems inside the vehicle may also cause security problems due to software defects or hardware failures. Therefore, how to identify and respond to information security risks of intelligent connected vehicles has become an issue that needs to be solved urgently.

This paper aims to review the identification and response technologies of information security risks of intelligent connected vehicles. 1. Analyze the main information security threats faced by intelligent connected vehicles, including network attacks, data leakage, privacy infringement, etc. In order to effectively identify and evaluate the information security risks faced by intelligent connected vehicles, the STRIDE threat analysis method is used to identify the threat system method of the four-layer model of the intelligent connected architecture, as well as the information security risk assessment of the whole life cycle. These methods help to fully understand the security threats of intelligent connected vehicles and provide a basis for formulating corresponding protection measures; 2. Discuss the key technologies currently used to identify and prevent risks, such as intrusion detection systems, secure communication protocols, data encryption and access control, etc. In response to the information security threats of intelligent connected vehicles, researchers have proposed a variety of protection technologies. For example, anomaly detection method of vehicle-mounted CAN bus network messages based on information entropy, anomaly detection method of vehicle-mounted CAN bus message data based on decision tree, certificateless public key authentication system, security mechanism of anti-sybil attack strategy, intrusion detection method based on physical layer signal characteristics, etc. These technologies are designed to improve the level of information security protection for intelligent connected vehicles, ensure vehicle driving safety and user data protection; 3. Propose research directions and potential technical solutions in the field of information security for intelligent connected vehicles, in order to provide reference and guidance for researchers and engineers in related fields. Although many studies have focused on the information security issues of intelligent connected vehicles, there are still some unresolved issues and challenges, such as the particularity of the dynamic network topology of the Internet of Vehicles, security issues in the on-board cloud, and the establishment of an information security evaluation system for intelligent connected vehicles. Therefore, future research needs to further explore more efficient and low-energy security mechanisms, as well as establish a more complete information security assessment and monitoring system.

Identification and response technology for information security risks in intelligent networked vehicles is a multifaceted, interdisciplinary research field that involves multiple links such as threat identification, risk assessment, and protection technology development. By comprehensively utilizing existing research results and technical means, the level of information security protection in intelligent networked vehicles can be effectively improved to ensure the safety and privacy of drivers and passengers.

The rapid development of intelligent connected vehicles is driven by the Internet of Things, cloud computing, artificial intelligence, big data and 5G technologies. The application of these technologies enables modern cars to achieve functions such as autonomous driving, intelligent transportation and smart cities. However, this also brings unprecedented security threats, such as remote intrusion and control. Therefore, it is particularly important to use the STRIDE threat analysis method to identify and protect intelligent connected vehicles from threats. The four-layer model threat identification system for intelligent connected vehicles based on the STRIDE threat analysis method is a method for comprehensive analysis and protection of information security risks of intelligent connected vehicles. This method divides intelligent connected vehicles into four different levels: perception device layer, network communication layer, control service layer and external connection layer to identify and analyze different information security issues that may be faced at each level.

The STRIDE threat model is a widely used security threat modeling method that divides the threats faced by the system into six categories: ① Impersonation/spoofing, illegal access to and use of other users' authentication information, such as usernames and passwords; ② Tampering, malicious modification of data, including changes to persistent data or data while it is transmitted over the network; ③ Repudiation, users deny that they have ever performed an operation, usually due to lack of adequate logging and auditing; ④ Information leakage, unauthorized access to sensitive information; ⑤ Denial of service, attacks that make the system or application unavailable; ⑥ Privilege escalation, attackers exploit vulnerabilities to escalate privileges and gain higher levels of access. This classification method helps ensure that the system has security attributes such as authentication, confidentiality, non-repudiation, integrity, availability, and authorization.

In the context of intelligent connected vehicles, the threat identification system based on the STRIDE threat analysis method can effectively improve the accuracy and coverage of threat analysis. For example, by refining the vehicle network architecture and dividing different intelligent connected vehicle in-vehicle communication domains according to information security risks and security levels, the security risks of the network communication layer can be reduced. In addition, the improved STRIDE model (iSTRIDE) proposes a hierarchical threat classification model that considers the threats faced by the system from two dimensions: one is the category to which the threat belongs, and the other is the location where the threat occurs. This improvement helps to more accurately determine where the system may face attacks from a certain type of threat, thereby providing a basis for selecting appropriate security solutions.

The four-layer model threat identification system for intelligent connected vehicles based on the STRIDE threat analysis method provides an effective method for the information security protection of intelligent connected vehicles through in-depth analysis of the intelligent connected vehicle architecture and detailed identification of threats at all levels. This method can not only improve the accuracy and coverage of threat analysis, but also help to formulate more targeted security protection measures to cope with security threats in increasingly complex network environments. The specific implementation steps and effect evaluation of the four-layer model threat identification system for intelligent connected vehicles based on the STRIDE threat analysis method can be divided into the following stages: ① System architecture analysis and threat identification: First, it is necessary to conduct a detailed analysis of the system architecture of intelligent connected vehicles, including its communication principles and mechanisms, as well as vehicle-to-vehicle communication, cloud communication (TSP) and V2X communication. This step is to clarify the security threats faced by intelligent connected vehicles, including physical attacks, close-range wireless attacks and long-range wireless attacks. ② Threat classification based on the STRIDE model: According to the STRIDE model, the identified threats are divided into six categories: counterfeit threats, tampering threats, denial threats, information leakage threats, denial of service threats and escalation of privilege threats. This step helps to systematically understand and deal with various types of security threats. ③ Determination of the location of threat occurrence: Further, according to the improved STRIDE model (iSTRIDE), the location of threat occurrence is divided into three places: system core, system boundary and system outside. This helps to select appropriate security solutions in a targeted manner. ④ Attack path modeling and risk assessment: Using the traditional way of building attack graphs or the attack graph modeling scheme based on Bayesian networks, the attack path of intelligent connected vehicles is modeled and risk analysis based on probabilistic reasoning is performed. This step aims to quantify the possibility and severity of threats and provide a basis for subsequent security risk assessment. ⑤ Security risk assessment and countermeasure formulation: Based on the threat identification and risk assessment results obtained in the above steps, corresponding risk elimination or mitigation methods are proposed for the identified threats. This includes but is not limited to strengthening identity authentication, protecting data confidentiality, ensuring system non-repudiation, integrity, availability and authorization and other security attributes. ⑥ Effect evaluation: Finally, by analyzing the experimental evaluation data, the usability of the model is verified and good results are achieved. This step is to ensure that the security measures taken can effectively improve the safety performance of intelligent connected vehicles.