Abstract
Risk management has become increasingly essential in all areas, and it represents a cornerstone of the Safety Management System. In principle, it brings together all the procedures to identify and evaluate risks to improve systems performance. With the development of the transportation system and the appearance of intelligent ones (ITS) that are changing citizens' mobility nowadays, the risks associated with them have also increased exponentially. In ITS, vehicles can reach 100% autonomy since they are equipped with sensors to move safely. The vehicle's architecture and embedded sensors enfold inherent vulnerabilities that attackers may exploit to craft malicious acts. In addition, vehicles communicate with each other and with the road infrastructure via vehicular adhoc network (VANET) and may use Internet connections, raising the risk that an attacker performs malicious actions and may take control of a vehicle to perform terrorist acts. This paper aims to draw attention to the risks associated with autonomous vehicles (AV) and the interest in evaluating flaws inherent in AV. For this purpose, our paper will extensively detail a new approach to assess the risk of attacks targeting autonomous vehicles. Our proposed approach will use a decision tree model to predict risk criticality based on the probability of attack success and its impact on the targeted system.
Highlights
The emergence of the smart city concept has brought several sub-concepts to the forefront, like transportation which is very important in smart cities
We will assess the first True of the decision tree, which means that the capability is very low, so the decision node will assess whether the attacker has knowledge about the system
We first started by identifying risk interfaces in the autonomous vehicles, we calculated the probability of attack success and evaluated the impact and severity of the attack; we can conclude the value of the risk
Summary
The emergence of the smart city concept has brought several sub-concepts to the forefront, like transportation which is very important in smart cities. In This article, we will study the assessment of risk in the context of an autonomous vehicle It is very important in an ITS to measure risk, prevent dangerous damages, and make our system more resistant to attacks. We will create a decision tree to predict the risk based on the probability of attack success and the impact of the attack. We will assess risk by measuring the probability of attack success using a decision tree, and we will describe the impact of attacks on an autonomous vehicle (AV). We will present the risk assessment by using a decision tree based on two criteria: the probability of attack success and the impact of attack and conclusion
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