Self-driving cars are going to be the main future mode of transportation. However, such systems like, any other cyber-physical system, are vulnerable to attack vectors and uncertainties. As a response, resilience-based approaches are being developed. However, the approaches lack a sound attack model that recognizes the attack vectors and vulnerabilities such a system would have and that does a proper severity analysis of such attacks. Moreover, the existing attack models are too generic. Currently, the domain lacks such specific work pertaining to self-driving cars. Given the technology and architecture of self-driving cars, the field requires a domain-specific attack model. This paper gives a review of the attack models and proposes a domain-specific attack model for self-driving cars. The proposed attack model, severity-based analytical attack model for resilience (SAAMR), provides attack analysis based on existing models. Also, a domainbased severity score for attacks is calculated. Further, the attacks are classified using the decision-tree method and predictions of the type of attacks are given using long short-term memory network. INDEX TERMSAttack-model, autonomous vehicles, cyber-attacks, resilience, security, self-driving car. NOMENCLATURE AT Adversarial attack tree. CAN Controller area network. CT Code modification/injection tree. CVE Common vulnerabilities and exposures. CVSS Common vulnerability scoring system. CWE Common weakness enumeration. DATMO Detection and tracking of moving objects. DDoS Distributed denial of service. DoS Denial of service. DT Decision tree. ECU Electronic controller unit. GPS Global positioning system. InV In-vehicle. ITS Intelligent transportation system. JT Jamming attack tree. LiDAR Light detection and ranging.
Increasing the resilience of traffic control systems is a priority for many important cities worldwide. This is due to the ever-increasing problems leading to different failures in such systems. We are witnessing the intensive introduction of new technologies that automatically manage traffic but are exposed to different kinds of attacks. There are also unpredictable increases in climatic changes and the number of cars in many cities. These factors will surely enhance the failure risks of such systems and consequently increase the damage caused by traffic jams and road accidents. In this paper, we introduce a resilient traffic control system that consists of three levels: sensor control, display, and light control. Each level has three (or more) versions and a dynamic voter. Hence, the introduced system is based on diversity and redundancy (replication), called N-versions. We propose two techniques for the introduced resilient traffic control system. The first technique uses N-versions and dynamic voters to vote between the outcomes in each level. The second technique uses N-versions, dynamic voters, and acceptance testing units. The overhead in the second technique is evidently greater than that of the first technique, but its resilience is better. A fine analytical study is conducted and shows that the first technique requires only three versions to reach the optimal results, bounded by 1/15 probability of having a faulty system. The second technique leads to better results, which can determine small probabilities.
<p>The paper is about the modeling attacks in self-driving vehicle, so to consider the faults and attacks prior to designing such a system.</p>
<p>The paper is about the modeling attacks in self-driving vehicle, so to consider the faults and attacks prior to designing such a system.</p>
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