Biblio

The technological development of automated vehicles opens novel cybersecurity threats and risks for road safety. Increased connectivity often results in increased risks of a cyber-security attacks, which is one of the biggest challenges for the automotive industry that undergoes a profound transformation. State of the art studies evaluated potential attacks and recommended possible measures, from technical and organizational perspective to face these challenges. In this position paper, we review these techniques and methods and show that some of the different solutions complement each other while others overlap or are even incompatible or contradictory. Based on this gap analysis, we advocate for the need of a comprehensive framework that integrates technical and organizational mitigation measures to enhance the cybersecurity of automotive vehicles.

The field of robotics has matured using artificial intelligence and machine learning such that intelligent robots are being developed in the form of autonomous vehicles. The anticipated widespread use of intelligent robots and their potential to do harm has raised interest in their security. This research evaluates a cyberattack on the machine learning policy of an autonomous vehicle by designing and attacking a robotic vehicle operating in a dynamic environment. The primary contribution of this research is an initial assessment of effective manipulation through an indirect attack on a robotic vehicle using the Q learning algorithm for real-time routing control. Secondly, the research highlights the effectiveness of this attack along with relevant artifact issues.

In this paper, we consider an attack on a string of automated vehicles, or platoons, from a game-theoretic standpoint. Game theory enables us to ask the question of optimality in an adversarial environment; what is the optimal strategy that an attacker can use to disrupt the operation of automated vehicles, considering that the defenders are also optimally trying to maintain normal operation. We formulate a zero-sum game and find optimal controllers for different game parameters. A platoon is then simulated and its closed loop stability is then evaluated in the presence of an optimal attack. It is shown that with the constraint of optimality, the attacker cannot significantly degrade the stability of a vehicle platoon in nominal cases. It is motivated that in order to have an optimal solution that is nearly unstable, the game has to be formulated almost unfairly in favor of the attacker.

Traditionally, the vehicle has been the extension of the manual ambulatory system, docile to the drivers' commands. Recent advances in communications, controls and embedded systems have changed this model, paving the way to the Intelligent Vehicle Grid. The car is now a formidable sensor platform, absorbing information from the environment, from other cars (and from the driver) and feeding it to other cars and infrastructure to assist in safe navigation, pollution control and traffic management. The next step in this evolution is just around the corner: the Internet of Autonomous Vehicles. Like other important instantiations of the Internet of Things (e.g., the smart building, etc), the Internet of Vehicles will not only upload data to the Internet with V2I. It will also use V2V communications, storage, intelligence, and learning capabilities to anticipate the customers' intentions and learn from other peers. V2I and V2V are essential to the autonomous vehicle, but carry the risk of attacks. This paper will address the privacy attacks to which vehicles are exposed when they upload private data to Internet Servers. It will also outline efficient methods to preserve privacy.

Autonomous vehicles must communicate with each other effectively and securely to make robust decisions. However, today's Internet falls short in supporting efficient data delivery and strong data security, especially in a mobile ad-hoc environment. Named Data Networking (NDN), a new data-centric Internet architecture, provides a better foundation for secure data sharing among autonomous vehicles. We examine two potential threats, false data dissemination and vehicle tracking, in an NDN-based autonomous vehicular network. To detect false data, we propose a four-level hierarchical trust model and the associated naming scheme for vehicular data authentication. Moreover, we address vehicle tracking concerns using a pseudonym scheme to anonymize vehicle names and certificate issuing proxies to further protect vehicle identity. Finally, we implemented and evaluated our AutoNDN application on Raspberry Pi-based mini cars in a wireless environment.

This paper proposes a prototype of a level 3 autonomous vehicle using Raspberry Pi, capable of detecting the nearby vehicles using an IR sensor. We make the first attempt to analyze autonomous vehicles from a microscopic level, focusing on each vehicle and their communications with the nearby vehicles and road-side units. Two sets of passive and active experiments on a pair of prototypes were run, demonstrating the interconnectivity of the developed prototype. Several sensors were incorporated into an emulation based on System-on-Chip to further demonstrate the feasibility of the proposed model.

Safety and non-safety applications in the external communication systems of self-driving vehicles require authentication of control data, cooperative awareness messages and notification messages. Traditional security systems can prevent attackers from hacking or breaking important system functionality in autonomous vehicles. This paper presents a novel security system designed to protect vehicular ad hoc networks in self-driving and semi-autonomous vehicles that is based on Integrated Circuit Metric technology (ICMetrics). ICMetrics has the ability to secure communication systems in autonomous vehicles using features of the autonomous vehicle system itself. This security system is based on unique extracted features from vehicles behaviour and its sensors. Specifically, features have been extracted from bias values of infrared sensors which are used alongside semantically extracted information from a trace file of a simulated vehicular ad hoc network. The practical experimental implementation and evaluation of this system demonstrates the efficiency in identifying of abnormal/malicious behaviour typical for an attack.

Semi-autonomous driver assists are already widely deployed and fully autonomous cars are progressively leaving the realm of laboratories. This evolution coexists with a progressive connectivity and cooperation, creating important safety and security challenges, the latter ranging from casual hackers to highly-skilled attackers, requiring a holistic analysis, under the perspective of fully-fledged ecosystems of autonomous and cooperative vehicles. This position paper attempts at contributing to a better understanding of the global threat plane and the specific threat vectors designers should be attentive to. We survey paradigms and mechanisms that may be used to overcome or at least mitigate the potential risks that may arise through the several threat vectors analyzed.

This paper proposes a taxonomy of autonomous vehicle handover situations with a particular emphasis on situational awareness. It focuses on a number of research challenges such as: legal responsibility, the situational awareness level of the driver and the vehicle, the knowledge the vehicle must have of the driver's driving skills as well as the in-vehicle context. The taxonomy acts as a starting point for researchers and practitioners to frame the discussion on this complex problem.