The Security of Autonomous Driving: Threats, Defenses, and Future Directions

被引:156
作者
Ren, Kui [1 ,2 ,3 ]
Wang, Qian [4 ]
Wang, Cong [5 ]
Qin, Zhan [1 ,2 ,3 ]
Lin, Xiaodong [6 ]
机构
[1] Zhejiang Univ, Inst Cyberspace Res, Hangzhou 310027, Peoples R China
[2] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310027, Peoples R China
[3] Zhejiang Univ, Alibaba Zhejiang Univ Joint Inst Frontier Technol, Hangzhou 310027, Peoples R China
[4] Wuhan Univ, Sch Cyber Sci & Engn, Wuhan 430072, Peoples R China
[5] City Univ Hong Kong, Dept Comp Sci, Hong Kong, Peoples R China
[6] Univ Guelph, Sch Comp Sci, Guelph, ON N1G 2W1, Canada
关键词
Laser radar; Sensors; Security; Global Positioning System; Autonomous vehicles; Jamming; Autonomous vehicles (AVs); in-vehicle protocol; in-vehicle systems; security; sensors; ATTACKS; PROTOCOL; VEHICLES; SIGNALS; PRIVACY; POWER;
D O I
10.1109/JPROC.2019.2948775
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Autonomous vehicles (AVs) have promised to drastically improve the convenience of driving by releasing the burden of drivers and reducing traffic accidents with more precise control. With the fast development of artificial intelligence and significant advancements of the Internet of Things technologies, we have witnessed the steady progress of autonomous driving over the recent years. As promising as it is, the march of autonomous driving technologies also faces new challenges, among which security is the top concern. In this article, we give a systematic study on the security threats surrounding autonomous driving, from the angles of perception, navigation, and control. In addition to the in-depth overview of these threats, we also summarize the corresponding defense strategies. Furthermore, we discuss future research directions about the new security threats, especially those related to deep-learning-based self-driving vehicles. By providing the security guidelines at this early stage, we aim to promote new techniques and designs related to AVs from both academia and industry and boost the development of secure autonomous driving.
引用
收藏
页码:357 / 372
页数:16
相关论文
共 121 条
[1]   Analysis of attacks against the security of keyless-entry systems for vehicles and suggestions for improved designs [J].
Alrabady, AL ;
Mahmud, SM .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2005, 54 (01) :41-50
[2]  
[Anonymous], LIDAR AUTONOMOUS TEC
[3]  
[Anonymous], P DEFCON
[4]  
[Anonymous], AUTONOMOUS VEHICLE S
[5]  
[Anonymous], P 7 IEEE INT C INF C
[6]  
[Anonymous], 2017, Federated learning: Collaborative machine learning without centralized training data.
[7]  
[Anonymous], JAM INTERCEPT REPLAY
[8]  
[Anonymous], 2018, LECT NOTES COMPUT SC, DOI DOI 10.1007/978-3-030-01258-8_10
[9]  
[Anonymous], LOCK IT LOSE IT
[10]  
[Anonymous], INTR KEEL COD HOOP T