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• W4313306320 abstract "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 domain-based 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." @default.
• W4313306320 created "2023-01-06" @default.
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• W4313306320 date "2023-01-01" @default.
• W4313306320 modified "2023-10-14" @default.
• W4313306320 title "Toward Attack Modeling Technique Addressing Resilience in Self-Driving Car" @default.
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• W4313306320 doi "https://doi.org/10.1109/access.2022.3233424" @default.
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