Security vulnerabilities of connected vehicle streams and their impact on cooperative driving

By M. Amoozadeh, Arun Raghuramu, Chen-Nee Chuah, Dipak Ghosal, H. Michael Zhang, Jeff Rowe, K. Levitt. IEEE Communications Magazine, 2015

391 citations recorded by the source corpus.

Autonomous vehicles capable of navigating unpredictable real-world environments with little human feedback are a reality today. Such systems rely heavily on onboard sensors such as cameras, radar/LIDAR, and GPS as well as capabilities such as 3G/4G connectivity and V2V/V2I communication to make real-time maneuvering decisions. Autonomous vehicle control imposes very strict requirements on the security of the communication channels used by the vehicle to exchange information as well as the control logic that performs complex driving tasks such as adapting vehicle velocity or changing lanes. This study presents a first look at the effects of security attacks on the communication channel as well as sensor tampering of a connected vehicle stream equipped to achieve CACC. Our simulation results show that an insider attack can cause significant instability in the CACC vehicle stream. We also illustrate how different countermeasures, such as downgrading to ACC mode, could potentially be used to improve the security and safety of the connected vehicle streams.

Subfields: Automotive Engineering, Control and Systems Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/9c96fa903e0c2c1b4dd44ecb68f29c7c89863462

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Citations in the corpus

Blockchain in internet of things: Challenges and Solutions (2016, background): “Numerous security vulnerabilities have been identified in connected devices ranging from smart locks [2] to vehicles [3].”
A systems and control perspective of CPS security (2019, background): “General transportation systems have been examined in [57]–[69], and industrial systems in [70]–[73].”
Policy and society related implications of automated driving: A review of literature and directions for future research (2017, background): “Amoozadeh et al. (2015) simulated message falsification and radio jamming attacks in a CACC vehicle stream, influencing the vehicles’ acceleration and space gap, respectively.”
Robust Deep Reinforcement Learning for Security and Safety in Autonomous Vehicle Systems (2018, background): “e of ITS data collected via a plethora of sensors and communication links. Reliability of such data is crucial to mitigate the chances of vehicle collisions and improve the vehicular ﬂow on the roads [4]. However, this reliance on communications and data processing renders AVs highly susceptible to ”