Biblio

The increase of autonomy in autonomous surface vehicles development brings along modified and new risks and potential hazards, this in turn, introduces the need for processes and methods for ensuring that systems are acceptable for their intended use with respect to dependability and safety concerns. One approach for evaluating software requirements for claims of safety is to employ an assurance case. Much like a legal case, the assurance case lays out an argument and supporting evidence to provide assurance on the software requirements. This paper analyses safety and security requirements relating to autonomous vessels, and regulations in the automotive industry and the marine industry before proposing a generic cybersecurity and safety assurance case that takes a general graphical approach of Goal Structuring Notation (GSN).

Autonomous vehicles (AVs) are capable of making driving decisions autonomously using multiple sensors and a complex autonomous driving (AD) software. However, AVs introduce numerous unique security challenges that have the potential to create safety consequences on the road. Security mechanisms require a benchmark suite and an evaluation framework to generate comparable results. Unfortunately, AVs lack a proper benchmarking framework to evaluate the attack and defense mechanisms and quantify the safety measures. This paper introduces BenchAV – a security benchmark suite and evaluation framework for AVs to address current limitations and pressing challenges of AD security. The benchmark suite contains 12 security and performance metrics, and an evaluation framework that automates the metric collection process using Carla simulator and Robot Operating System (ROS).

With the gradually popular high-speed wireless networks and 5G environments, the quality and reliability of network services will be suited for mobile vehicles. In addition to communicating information between vehicles, they can also communicate information with surrounding roadside equipment, pedestrians or traffic signs, and thus improve the road safety of passers-by.Recently, various countries have continuously invested in research on autonomous driving and unmanned vehicles. The open communication environment of the Internet of Vehicles in 5G will expose all personal information in the field of wireless networks. This research is based on the consideration of information security and personal data protection. We will focus on how to protect the real-time transmission of information between mobile vehicles to prevent from imbedding or altering important transmission information by unauthorized vehicles, drivers or passers-by participating in communications. Moreover, this research proposes a multi-level security key management agreement based on a dynamic M-tree structure for Internet of Vehicles to achieve flexible and scalable key management on large-scale Internet of Vehicles.

One of the typical autonomous driving systems is a human-machine cooperative system that intervenes in the driver operation. The autonomous driving needs to make consideration of the driver individuality in addition to safety. This paper considers a human-machine cooperative system balancing safety with the driver individuality using the Human-In-The-Loop System (HITLS) for rear-wheel steering control. This paper assumes that it is safe for HITLS to follow the target side-slip angle and target angular velocity without conflicts between the controller and driver operations. We propose HITLS using the primal-dual algorithm and the internal model control (IMC) type I-PD controller. In HITLS, the signal expander delimits the human-selectable operating range and the controller cooperates stably the human operation and automated control in that range. The primal-dual algorithm realizes the driver and the signal expander. Our outcomes are the making of the rear-wheel steering system which converges to the target value while reflecting the driver individuality.

Over recent years, we have seen a significant rise in popularity of autonomous vehicle. Several researches have shown the severity of security threats that autonomous vehicles face -for example, Miller and Valasek (2015) were able to remotely take complete control over a 2014 Jeep Cherokee in a so called "Jeephack" [1]. This paper analyses the threats that the Electrical and Electronic (E/E) architecture of an autonomous vehicle has to face and rank those threats by severity. To achieve this, the Microsoft's STRIDE threat analysis technique was applied and 13 threats were identified. These are sorted by their Common Vulnerability Scoring System (CVSS) scores. Potential mitigation methods are then suggested for the five topmost severe threats.

Emerging trends that are shaping the future of the automotive industry include electrification, autonomous driving, sharing, and connectivity, and these trends keep changing annually. Thus, the automotive industry is shifting from mechanical devices to electronic control devices, and is not moving to Internet of Things devices connected to 5G networks. Owing to the convergence of automobile-information and communication technology (ICT), the safety and convenience features of automobiles have improved significantly. However, cyberattacks that occur in the existing ICT environment and can occur in the upcoming 5G network are being replicated in the automobile environment. In a hyper-connected society where 5G networks are commercially available, automotive security is extremely important, as vehicles become the center of vehicle to everything (V2X) communication connected to everything around them. Designing, developing, and deploying information security techniques for vehicles require a systematic security-risk-assessment and management process throughout the vehicle's lifecycle. To do this, a security risk analysis (SRA) must be performed, which requires an analysis of cyber threats on automotive vehicles. In this study, we introduce a cyber kill chain-based cyberattack analysis method to create a formal vulnerability-analysis system. We can also analyze car-hacking studies that were conducted on real cars to identify the characteristics of the attack stages of existing car-hacking techniques and propose the minimum but essential measures for defense. Finally, we propose an automotive common-vulnerabilities-and-exposure system to manage and share evolving vehicle-related cyberattacks, threats, and vulnerabilities.

Vehicular Ad Hoc Network (VANET) is a promising technology for autonomous driving as it provides many benefits and user conveniences to improve road safety and driving comfort. Sybil attack is one of the most serious threats in vehicular communications because attackers can generate multiple forged identities to disseminate false messages to disrupt safety-related services or misuse the systems. To address this issue, we propose a Sybil attack detection scheme using ADAS (Advanced Driving Assistant System) sensors installed on modern passenger vehicles, without the assistance of trusted third party authorities or infrastructure. Also, a deep learning based object detection technique is used to accurately identify nearby objects for Sybil attack detection and the multi-step verification process minimizes the false positive of the detection.

Fully autonomous driving is one if not the killer application for the upcoming decade of real-time systems. However, in the presence of increasingly sophisticated attacks by highly skilled and well equipped adversarial teams, autonomous driving must not only guarantee timeliness and hence safety. It must also consider the dependability of the software concerning these properties while the system is facing attacks. For distributed systems, fault-and-intrusion tolerance toolboxes already offer a few solutions to tolerate partial compromise of the system behind a majority of healthy components operating in consensus. In this paper, we present a concept of an intrusion-tolerant architecture for autonomous driving. In such a scenario, predictability and recovery challenges arise from the inclusion of increasingly more complex software on increasingly less predictable hardware. We highlight how an intrusion tolerant design can help solve these issues by allowing timeliness to emerge from a majority of complex components being fast enough, often enough while preserving safety under attack through pre-computed fail safes.

The pace of technological development in automotive and transportation has been accelerating rapidly in recent years. Automation of driver assistance systems, autonomous driving, increasing vehicle connectivity and emerging inter-vehicular communication (V2V) are among the most disruptive innovations, the latter of which also raises numerous unprecedented security concerns. This paper is focused on the security of V2V communication in vehicle ad-hoc networks (VANET) with the main goal of identifying realistic attack scenarios and evaluating their impact, as well as possible security countermeasures to thwart the attacks. The evaluation has been done in OMNeT++ simulation environment and the results indicate that common attacks, such as replay attack or message falsification, can be eliminated by utilizing digital signatures and message validation. However, detection and mitigation of advanced attacks such as Sybil attack requires more complex approach. The paper also presents a simple detection method of Sybil nodes based on measuring the signal strength of received messages and maintaining reputation of sending nodes. The evaluation results suggest that the presented method is able to detect Sybil nodes in VANET and contributes to the improvement of traffic flow.

In autonomous driving, security issues from robotic and automotive applications are converging toward each other. A novel approach for deriving secret keys using a lightweight cipher in the firmware of low-end control units is introduced. By evaluating the method on a typical low-end automotive platform, we demonstrate the reusability of the cipher for message authentication. The proposed solution counteracts a known security issue in the robotics and automotive domain.