The Security of Autonomous Driving: Threats, Defences, and Future
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1 The Security of Autonomous Driving: Threats, Defences, and Future Directions Kui Ren, Fellow, IEEE, Qian Wang, Senior Member, IEEE, Cong Wang, Senior Member, IEEE, Zhan Qin, Member, IEEE, and Xiaodong Lin, Fellow, IEEE Abstract—Autonomous vehicles (AVs) have promised to drasti- Various Sensors cally improve the convenience of driving by releasing the burden of drivers and reducing traffic accidents with more precise Camera, Radar, LiDAR, GPS, Ultrasonic Sensor control. With the fast development of artificial intelligence and In-Vehicle Systems significant advancements of IoT technologies, we have witnessed VCS In-Vehicle Protocol the steady progress of autonomous driving over the recent years. As promising as it is, the march of autonomous driving Immobilizer CAN technologies also faces new challenges, among which security Keyless Entry LIN is the top concern. In this article, we give a systematic study on the security threats surrounding autonomous driving, from Critical Components FlexRay the angles of perception, navigation, and control. In addition to the in-depth overview of these threats, we also summarise the corresponding defence strategies. Furthermore, we discuss future research directions about the new security threats, especially those related to deep learning based self-driving vehicles. By Fig. 1: The three types of attack surfaces of an AV. 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. Recently, with the prevalence of artificial intelligence (AI) Index Terms—Autonomous Vehicles, Security, Sensors, In- and Internet of Things (IoT) technologies, autonomous driving Vehicle Systems, In-Vehicle Protocol. has gained steady improvements, and is getting more and more intelligent to precisely sense environments in the real world, quickly analyze the sensor data, and autonomously I. INTRODUCTION make complex decisions. In the foreseeable future, AVs are Since the CMU Navlab group built the first computer- widely believed to be one of the most popular AI applications controlled vehicles for automated driving in 1984 [1], many in people’s daily lives. For instance, IHS Markit predicts that researchers have promoted autonomous vehicle (AV) develop- the annual sales of AVs will exceed 33 million in 2040 [3]. ments. One noteworthy breakthrough was in 1994, when the group of UniBw Munich and the group of Daimler-Benz have As promising as it is, the fast development of autonomous co-developed an AV that could reach the speed up to 130 driving technologies also faces new challenges, among which km/h [2]. That very AV could automatically track different security is the top concern. Specifically, before the wide lane markings and decide when to change between lanes. adoption of AVs on the road facing realistic traffic conditions, the security and trustworthiness of AVs must be guaranteed through all kinds of technical assurances. As we know, AVs Kui Ren’s research is supported in part by the NSFC under Grant 61772236 are often equipped with varieties of functionally-rich sensors, and Zhejiang Key R&D Plan under Grant 2019C03133. Qian Wang’s research such as cameras, Radars, GPS, etc., to perceive its surrounding is supported in part by the NSFC under Grants 61822207 and U1636219, the Equipment Pre-Research Joint Fund of Ministry of Education of China (Youth environments. The data captured by sensors are fed into the Talent) under Grant 6141A02033327, the Outstanding Youth Foundation of AV’s computing system for rounds of complicated processing Hubei Province under Grant 2017CFA047, and the Fundamental Research and calculations in order to enable the autonomous control Funds for the Central Universities under Grant 2042019kf0210. Cong Wang’s research is supported in part by the Research Grants Council of Hong Kong of the vehicle, including the braking mechanism as well as under Grants CityU C1008-16G, and by the NSFC under Grant 61572412. the engine. Hence, AVs heavily rely on the sensor data to Zhan Qin’s work is supported in part by Major Scientific Research Project of make the right driving decisions, which inevitably enlarges the Zhejiang Lab under Grant 2018FD0ZX01. (Corresponding author: Kui Ren) K. Ren and Z. Qin are with the Institute of Cyberspace Research, the College potential threat surface and incurs serious security risks from of Computer Science and Technology, and Alibaba-Zhejiang University Joint sensors [4]. In addition, the systems responsible for in-vehicle Institute of Frontier Technologies, Zhejiang University, P.R. China. Email: access and control (e.g., voice controllable systems and keyless fkuiren, [email protected]. Q. Wang is with the School of Cyber Science and Engineering, Wuhan entry systems), and the protocols indispensable for in-vehicle University, P. R. China. Email: [email protected]. network operations (e.g., Controller Area Networks (CAN)), C. Wang is with Department of Computer Science, City University of Hong also require effective security countermeasures against various Kong, Hong Kong SAR, P. R. China. Email: [email protected]. X. Lin is with the School of Computer Science, University of Guelph, Canada. attacks whilst providing critical and decisive functionalities for Email: [email protected]. AVs. 2 TABLE I: Various types of sensors. Sensors Signal Working Area Principle Usage GPS Microwave Global Passive Navigation Pedestrian detection LiDAR Infrared laser Medium range Active Collision avoidance Collision avoidance MMW Radar Microwave Long range Active Adptive cruise control Ultrasonic Sensor Ultrasound Proximity Active Parking assistance Traffic sign recognition Camera Visible light Short range Passive Lane detection Obstacle detection In Fig. 1, we briefly categorize the broadly defined security threats surrounding an autonomous vehicle into three classes. Ultrasound : Parking Assistance The first type contains different kinds of sensors equipped in Camera : Traffic Sign Recognition LiDAR : Collision Avoidance the vehicle, which perceive the surrounding road conditions. MMW Radar : Adaptive Cruise Control The sensor data are further used to guide driving without GPS : Navigation human intervention. Once they are jammed or spoofed by false signals, the autonomous driving car will lose the ability of precisely sensing the environments. The second one includes various in-vehicle access and control systems, e.g., the vehicle immobilizer, the keyless entry system, critical control com- ponents, and the voice controllable system (VCS). These in- vehicle systems guarantee the security of physical car access and human-vehicle interaction. If these in-vehicle access and control systems are broken, it would lead to critical security flaws and incidents to AV, as serious as a matter of life and Fig. 2: Sensors embed in autonomous vehicle. death. The last is about the in-vehicle network protocols, such as the Local Interconnect Network (also known as LIN), the CAN mentioned above, FlexRay, and more. Any vulnerability In this section, we introduce various types of attacks against of the protocols could be exploited through telematics modules most common sensors in AVs and provide some corresponding and further magnified remotely by the attackers to illegally defense strategies. control the vehicles. In this article, we first give a systematic study on the cat- A. Various Types of Attacks egories of security threats, particularly from the perspectives 1) Attacks against GPS: GPS is indispensable in the navi- of perception, navigation, and control. We then respectively gation of AVs. Relaying on position got from GPS and aided summarize the corresponding defense strategies. Last but not by a precise map, AVs can choose an optimized, shortest least, we highlight a few crucial open problems, especially path from one location to another location, even without those related to deep learning based self-driving vehicles, and any previous knowledge. This is essential for AVs to work discuss future research directions. We believe that our work correctly without the assistance of drivers. Meanwhile, this can encourage new techniques and designs related to defenses also exposes vulnerabilities to malicious attackers. against threats posed to AVs, and push forward the frontier The attacks toward GPS have been studied widely in the and future development of secure autonomous driving. past decade [5]–[11]. Existing attacks, like [6], [9], [12], [13], demonstrate that the GPS attacks are practical. There are II. POTENTIAL THREATS OF SENSORS mainly two kinds of GPS attacks: spoofing and jamming. GPS signals from satellites are weak due to long-distance traveling When AVs cruise on the road, it is essential for AVs to [6]. Hence, the jamming attack is much easy to be launched sense the environmental circumstances precisely, due to the by using stronger signals in the same frequencies . In the lack of drivers’ control. Various sensors, like GPS, ultrasonic following part, we focus on introducing spoofing attacks since sensor, LiDAR (Light Detection and Ranging), and MMW they are more threatening than jamming. (Millimeter Wave) Radar, are “eyes” indispensable for AVs. Spoofing aims to drag victims off to incorrect position (and Fig. 2 illustrates sensors embedded in AVs, and Table I time) by fabricating spurious signals which deviate the correct shows a generic description of these sensors as well as the position of victims. A simple strategy that could be easily