Biblio

Software integration in modern vehicles is continuously expanding. This is due to the fact that vehicle manufacturers are always trying to enhance and add more innovative and competitive features that may rely on complex software functionalities. However, these features come at a cost. They amplify the security vulnerabilities in vehicles and lead to more security issues in today's automobiles. As a result, the need for identifying vulnerable components in a vehicle software system has become crucial. Security experts need to know which components of the vehicle software system can be exploited for attacks and should focus their testing and inspection efforts on it. Nevertheless, it is a challenging and costly task to identify these weak components in a vehicle's system. In this paper, we propose some security vulnerability metrics for connected vehicles that aim to assist software testers during the development life-cycle in order to identify the frail links that put the vehicle at highsecurity risks. Vulnerable function assessment can give software testers a good idea about which components in a connected vehicle need to be prioritized in order to mitigate the risk and hence secure the vehicle. The proposed metrics were applied to OpenPilot - a software that provides Autopilot feature - and has been integrated with 48 different vehicles.. The application shows how the defined metrics can be effectively used to quantitatively measure the vulnerabilities of a vehicle software system.

Intelligent connected vehicle system tightly integrates computing, communication, and control strategy. It can increase the traffic throughput, minimize the risk of accidents and reduce the energy consumption. However, because of the openness of the vehicular ad hoc network, the system is vulnerable to cyber-attacks and may result in disastrous consequences. Hence, it is interesting in design of the connected vehicular systems to be resilient to the sensor attacks. The paper focuses on the estimation and control of the intelligent connected vehicle systems when the sensors or the wireless channels of the system are attacked by attackers. We give the upper bound of the corrupted sensors that can be corrected and design the state estimator to reconstruct the initial state by designing a closed-loop controller. Finally, we verify the algorithm for the connected vehicle system by some classical simulations.

The paper considers data security and privacy issues in intelligent transportation systems which involve data streams coming out from individual vehicles to road side units. In this environment, there are issues in regards to the scalability of key management and computation limitations at the edge of the network. To address these issues, we suggest the formation of groups in the vehicular layer, where a group leader is assigned to communicate with group members and the road side unit. We propose a lightweight permutation mechanism for preserving the confidentiality and privacy of sensory data.

Complex embedded systems often contain hard to find vulnerabilities which, when exploited, have potential to cause severe damage to the operating environment and the user. Given that threats and vulnerabilities can exist within any layer of the complex eco-system, OEMs face a major challenge to ensure security throughout the device life-cycle To lower the potential risk and damage that vulnerabilities may cause, OEMs typically perform application threat analysis and security modelling. This process typically provides a high level guideline to solving security problems which can then be implemented during design and development. However, this concept presents issues where new threats or unknown vulnerability has been discovered. To address this issue, we propose a policy-based security modelling approach, which utilises a configurable policy engine to apply new policies that counter serious threats. By utilising this approach, the traditional security modelling approaches can be enhanced and the consequences of a new threat greatly reduced. We present a realistic use case of connected car, applying several attack scenarios. By utilising STRIDE threat modelling and DREAD risk assessment model, adequate policies are derived to protect the car assets. This approach poses advantages over the standard approach, allowing a policy update to counter a new threat, which may have otherwise required a product redesign to alleviate the issue under the traditional approach.

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.

With the advances in the areas of mobile computing and wireless communications, V2X systems have become a promising technology enabling deployment of applications providing road safety, traffic efficiency and infotainment. Due to their increasing popularity, V2X networks have become a major target for attackers, making them vulnerable to security threats and network conditions, and thus affecting the safety of passengers, vehicles and roads. Existing research in V2X does not effectively address the safety, security and performance limitation threats to connected vehicles, as a result of considering these aspects separately instead of jointly. In this work, we focus on the analysis of the tradeoffs between safety, security and performance of V2X systems and propose a dynamic adaptability approach considering all three aspects jointly based on application needs and context to achieve maximum safety on the roads using an Internet of vehicles. Experiments with a simple V2V highway scenario demonstrate that an adaptive safety/security approach is essential and V2X systems have great potential for providing low reaction times.

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.