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Examining Modern Data Security and Privacy Protocols in Autonomous Vehicles


Affiliations
1 Department of Computer Science, University of Wisconsin, Eau Claire, Eau Claire, United States
 

A fully automated, self-driving car can perceive its environment, determine the optimal route, and drive unaided by human intervention for the entire journey. Connected autonomous vehicles (CAVs) have the potential to drastically reduce accidents, travel time, and the environmental impact of road travel. Such technology includes the use of several sensors, various algorithms, interconnected network connections, and multiple auxiliary systems. CAVs have been subjected to attacks by malicious users to gain/deny control of one or more of its various systems. Data security and data privacy is one such area of CAVs that has been targeted via different types of attacks. The scope of this study is to present a good background knowledge of issues pertaining to different attacks in the context of data security and privacy, as well present a detailed review and analysis of eight very recent studies on the broad topic of security and privacy related attacks. Methodologies including Blockchain, Named Data Networking, Intrusion Detection System, Cognitive Engine, Adversarial Objects, and others have been investigated in the literature and problem- and context-specific models have been proposed by their respective authors.

Keywords

Security & Privacy, Autonomous Vehicles, Blockchain, Connected Autonomous Vehicles.
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Abstract Views: 347

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  • Examining Modern Data Security and Privacy Protocols in Autonomous Vehicles

Abstract Views: 347  |  PDF Views: 205

Authors

Mingfu Huang
Department of Computer Science, University of Wisconsin, Eau Claire, Eau Claire, United States
Rushit Dave
Department of Computer Science, University of Wisconsin, Eau Claire, Eau Claire, United States
Nyle Siddiqui
Department of Computer Science, University of Wisconsin, Eau Claire, Eau Claire, United States
Naeem Seliya
Department of Computer Science, University of Wisconsin, Eau Claire, Eau Claire, United States

Abstract


A fully automated, self-driving car can perceive its environment, determine the optimal route, and drive unaided by human intervention for the entire journey. Connected autonomous vehicles (CAVs) have the potential to drastically reduce accidents, travel time, and the environmental impact of road travel. Such technology includes the use of several sensors, various algorithms, interconnected network connections, and multiple auxiliary systems. CAVs have been subjected to attacks by malicious users to gain/deny control of one or more of its various systems. Data security and data privacy is one such area of CAVs that has been targeted via different types of attacks. The scope of this study is to present a good background knowledge of issues pertaining to different attacks in the context of data security and privacy, as well present a detailed review and analysis of eight very recent studies on the broad topic of security and privacy related attacks. Methodologies including Blockchain, Named Data Networking, Intrusion Detection System, Cognitive Engine, Adversarial Objects, and others have been investigated in the literature and problem- and context-specific models have been proposed by their respective authors.

Keywords


Security & Privacy, Autonomous Vehicles, Blockchain, Connected Autonomous Vehicles.

References