TY - JOUR
T1 - Cyber Attacks via Consumer Electronics
T2 - Studying the Threat of Covert Malware in Smart and Autonomous Vehicles
AU - Haghighi, Mohammad Sayad
AU - Farivar, Faezeh
AU - Jolfaei, Alireza
AU - Asl, Azin Bayrami
AU - Zhou, Wei
PY - 2023/11/1
Y1 - 2023/11/1
N2 - In Industry 5.0, man and machine work alongside each other in production, but smart and autonomous vehicles are examples that show this notion is now being extended to the end consumers. In 2015, a Jeep was remotely hacked through its head unit. This incident drew the public attention to vehicles security and showed how entertainment/infotainment consumer electronics can be used to intrude vehicles. In this paper, we study a novel covert attack that can be launched by malwares spreading through Intelligent Transportation Systems, e.g. via consumer electronics. This malware infects a vehicle module, like the Adaptive Cruise Controller (ACC), and manipulates its setting in a way that is not noticeable to human observers, but gives rise to accidents statistics. We show how this is done and analyze the effect mathematically. We also propose a new Intrusion Detection System (IDS) whose architecture is non-disruptive and can be readily adopted by car manufacturers. We evaluate our proposal with real-world datasets. We demonstrate how a malware/attacker can engineer the crash statistics by manipulating the safe distance value in cruise control scenarios. Then, we put an anomaly-based IDS for ACC modules into test and show how it can effectively detect such covert attacks.
AB - In Industry 5.0, man and machine work alongside each other in production, but smart and autonomous vehicles are examples that show this notion is now being extended to the end consumers. In 2015, a Jeep was remotely hacked through its head unit. This incident drew the public attention to vehicles security and showed how entertainment/infotainment consumer electronics can be used to intrude vehicles. In this paper, we study a novel covert attack that can be launched by malwares spreading through Intelligent Transportation Systems, e.g. via consumer electronics. This malware infects a vehicle module, like the Adaptive Cruise Controller (ACC), and manipulates its setting in a way that is not noticeable to human observers, but gives rise to accidents statistics. We show how this is done and analyze the effect mathematically. We also propose a new Intrusion Detection System (IDS) whose architecture is non-disruptive and can be readily adopted by car manufacturers. We evaluate our proposal with real-world datasets. We demonstrate how a malware/attacker can engineer the crash statistics by manipulating the safe distance value in cruise control scenarios. Then, we put an anomaly-based IDS for ACC modules into test and show how it can effectively detect such covert attacks.
KW - Industry 5.0
KW - Intrusion Detection System
KW - worm
KW - intelligent transportation systems
KW - adaptive cruise control
KW - security
KW - Consumer electronics
UR - http://www.scopus.com/inward/record.url?scp=85166755736&partnerID=8YFLogxK
U2 - 10.1109/TCE.2023.3297965
DO - 10.1109/TCE.2023.3297965
M3 - Article
AN - SCOPUS:85166755736
SN - 0098-3063
VL - 69
SP - 825
EP - 832
JO - IEEE Transactions on Consumer Electronics
JF - IEEE Transactions on Consumer Electronics
IS - 4
ER -