Blockchain-based security attack resilience schemes for autonomous vehicles in industry 4.0: A systematic review

Vulnerabilities and safety assurance methods in Cyber-Physical Systems: A comprehensive review

Internet of Things (IoT) is turning into an undeniably developing point of discussion in both research and industrial fields A key area that is witnessing a quick development in the utilization of IoT devices is the Internet of Vehicles (IoV), which allows information exchange among vehicles and infrastructures Notably, Artificial Intelligence (AI) has been widely adopted for solving challenging vehicular problems and managing the IoV infrastructure Despite the advantages AI carries for IoV, its deployment can be negatively affected by lacking computation resources and processing unreliable data On the other hand, Blockchain is a decentralized and distributed peer-to-peer network architecture that can be employed to empower security and resist against undesirable data modification However, integrating both technologies (ie, AI and Blockchain) exhausts, even more, the IoV infrastructure Therefore, we present in this paper an overview discussing the AI and Blockchain approaches and models for IoV and propose a new Vehicular Edge Computing based architecture embedding both technologies and overcoming the aforementioned limitations We then discuss the main challenges and give notice to the concerned parties and stakeholders about promising directions that arise from enabling the three technologies for providing smart, secure, and efficient IoV

AI, Blockchain, and Vehicular Edge Computing for Smart and Secure IoV: Challenges and Directions

Attacks and defences on intelligent connected vehicles: a survey

Vehicular ad hoc networks (VANETs) are a subsystem of the proposed intelligent transportation system (ITS) that enables vehicles to communicate over the wireless communication infrastructure. VANETs are used in multiple applications, such as improving traffic safety and collision prevention. The use of VANETs makes the network vulnerable to various types of attacks, such as denial of service (DoS) and distributed denial of service (DDoS). Many researchers are now interested in adding a high level of security to VANETs. Machine learning (ML) methods were used for constructing a high level of security capabilities based on intrusion detection systems (IDSs). Furthermore, the vast majority of existing research is based on NSL-KDD or KDD-CUP99 datasets. Recent attacks are not present in these datasets. As a result, we employed a realistic dataset called ToN-IoT that derived from a large-scale, heterogeneous IoT network. This work tested various ML methods in both binary and multi-class classification problems. We used the Chi-square (Chi2) technique was used for feature selection and the Synthetic minority oversampling technique (SMOTE) for class balancing. According to the results, the XGBoost method outperformed other ML methods.

Intrusion Detection System Using Machine Learning for Vehicular Ad Hoc Networks Based on ToN-IoT Dataset

Autonomous vehicles (AVs) attract a lot of attention recently. They are expected to assist/replace the human drivers in maneuvering the vehicle, thereby reducing the likelihood of road accidents caused by human error, as a means to improve the road traffic safety. However, AVs have their inherent safety and security challenges, which have to be addressed before they are ready for wide adoption. This paper presents an overview of recent research on AV safety failures and security attacks, as well as the available safety and security countermeasures.

A review on safety failures, security attacks, and available countermeasures for autonomous vehicles

Safety and security are two inter-dependent key properties of autonomous vehicles. They are aimed at protecting the vehicles from accidental failures and intentional attacks, which could lead to injuries and loss of lives. The selection of safety and security countermeasures for autonomous vehicles depends on the driving automation levels, defined by the international standard SAE J3016. However, current vehicle safety standards ISO 26262 do not take the driving automation levels into consideration. We propose an approach for integrating autonomous vehicle safety and security processes, which is compliant with the international standards SAE J3016, SAE J3061, and ISO 26262, and which considers driving automation levels. It incorporates the SystemTheoretic Process Analysis method into autonomous vehicle safety analysis, and uses the Six-Step Model as a backbone for achieving integration and alignment among safety and security processes and artefacts throughout the entire autonomous vehicle’s lifecycle. Keywords–Autonomous vehicle; safety; security; Six-Step Model; STPA.

Integrating Autonomous Vehicle Safety and Security Analysis Using STPA Method and the Six-Step Model

Human error has been statistically proven to be the primary cause of road accidents. This undoubtedly is a contributory cause of the rising popularity of autonomous vehicles as they are presumably able to maneuver appropriately/optimally on the roads while diminishing the likelihood of human error and its repercussion. However, autonomous vehicles are not ready for widespread adoption because their safety and security issues are yet to be thoroughly investigated/addressed. Little literature could be found on collaborative analysis of safety and security of autonomous vehicles. This paper proposes a framework for analyzing both safety and security issues, which includes an integrated safety and security method (S&S) with international vehicle safety and security standards ISO 26262 and SAE J3061. The applicability of the proposed framework is demonstrated using an example of typical autonomous vehicle model. Using this framework, one can clearly understand the vehicle functions, structure, the associated failures and attacks, and also see the vulnerabilities that are not yet addressed by countermeasures, which helps to improve the in-vehicle safety and security from researching and engineering perspectives.

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Collaborative Analysis Framework of Safety and Security for Autonomous Vehicles

Attack trees are used in cybersecurity analysis to give an analyst a view of all the ways in which an attack can be carried out. Attack trees can become large, and developing them by hand can be tedious and error-prone. In this paper the automated generation of attack trees is considered. The method proposed is based on a library of attack templates – parameterisable patterns of attacks such as denial of service or eavesdropping – and that also uses an abstract model of the network architecture under attack. A pseudocode implementation of the method is also presented. The example application given is from the automotive domain and using an architecture consisting of linked CAN networks – a network configuration found in virtually every current vehicle.

A Template-Based Method for the Generation of Attack Trees

Automated Vehicles (AVs), the self driving vehicles, are expected to outperform human drivers and improve road safety in the near future. However, to achieve these goals, they need to communicate with each other and the other road participants and coordinate their actions. The systems of connected cooperative AVs are called Cooperative Intelligent Transport Systems (C-ITS). Similar to AVs, C-ITS are vulnerable to failures and cyberattacks. In our previous work, we proposed a method US2 for AV risk analysis. This paper extends US2 and presents a method for integrated C-ITS safety and cybersecurity risk analysis. It takes into consideration automotive safety and cybersecurity standards ISO 26262 and SAE J3061, and utilizes the elements of the previously proposed risk analysis methods US2, EVITA, TVRA, and RACE.

Lin Shen Liew

Papers

A review on safety failures, security attacks, and available countermeasures for autonomous vehicles

Integrating Autonomous Vehicle Safety and Security Analysis Using STPA Method and the Six-Step Model

Collaborative Analysis Framework of Safety and Security for Autonomous Vehicles

A Template-Based Method for the Generation of Attack Trees

Integrated Safety and Cybersecurity Risk Analysis of Cooperative Intelligent Transport Systems