Situational Crime Prevention for Automotive Cybersecurity

被引:3
作者
Polanco, Nick [1 ]
Cheng, Betty [1 ]
机构
[1] Michigan State Univ, E Lansing, MI 48824 USA
来源
ACM/IEEE 25TH INTERNATIONAL CONFERENCE ON MODEL DRIVEN ENGINEERING LANGUAGES AND SYSTEMS, MODELS 2022 COMPANION | 2022年
关键词
Sociotechnical; Situational Crime Prevention; Autonomous Vehicles; Self-Driving Cars; Human-Based; Cybersecurity; SECURITY THREATS;
D O I
10.1145/3550356.3561600
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
The increase in number and types of various stakeholders interacting with self-driving vehicles expands the relevant automotive cybersecurity attack vectors that can be compromised. Furthermore, given the prominent role that human behavior plays in the lifetime of a vehicle, social and human-based factors must be considered in tandem with the technical factors when addressing cybersecurity. A focus on informing and enabling stakeholders and their corresponding actions promotes security of the vehicle through a human-focused and technology-enabled approach. Example stakeholders include the consumer operating the vehicle, the technicians working on the car, and the engineers designing the software. Strategies can be applied in both a social and technical manner to increase preventative security measures for autonomous vehicles by leveraging theoretical foundations from the criminology domain. In this work we harness a criminology theory approach to crime prevention, where we synergistically combine cybercrime theory, human factors, and technical solutions to develop a cybercrime prevention framework that accounts for a range of stakeholders relevant to an autonomous vehicle domain.
引用
收藏
页码:562 / 568
页数:7
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