ECU Fingerprinting through Parametric Signal Modeling and Artificial Neural Networks for In-vehicle Security against Spoofing Attacks

Hafeez, Azeem; Topolovec, Kenneth; Awad, S.S.

doi:10.1109/icenco48310.2019.9027298

Cited by 20 publications

(4 citation statements)

References 22 publications

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“…Recent studies also suggest that adversarial examples can be employed to confuse autonomous vehicles by manipulating traffic signs or altering the segment of pedestrians in an object detection system (Xie et al, 2017). Several types of attacks are topics of extensive discussion in literature (Assion et al, 2019; Hafeez et al, 2019; Qayyum et al, 2020; Ren et al, 2020a, 2020b; Sadeghi et al, 2020; Sharma et al, 2019). Several adversarial examples generating methods will be discussed in the next subsection.…”

Section: Machine Learning For Cavs Cybersecuritymentioning

confidence: 99%

Machine learning and blockchain technologies for cybersecurity in connected vehicles

Ahmad,

Zia,

Naqvi

et al. 2023

WIREs Data Min & Knowl

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Future connected and autonomous vehicles (CAVs) must be secured against cyberattacks for their everyday functions on the road so that safety of passengers and vehicles can be ensured. This article presents a holistic review of cybersecurity attacks on sensors and threats regarding multi‐modal sensor fusion. A comprehensive review of cyberattacks on intra‐vehicle and inter‐vehicle communications is presented afterward. Besides the analysis of conventional cybersecurity threats and countermeasures for CAV systems, a detailed review of modern machine learning, federated learning, and blockchain approach is also conducted to safeguard CAVs. Machine learning and data mining‐aided intrusion detection systems and other countermeasures dealing with these challenges are elaborated at the end of the related section. In the last section, research challenges and future directions are identified.This article is categorized under: Commercial, Legal, and Ethical Issues > Security and Privacy Technologies > Machine Learning Technologies > Internet of Things

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Section: Machine Learning For Cavs Cybersecuritymentioning

confidence: 99%

Machine learning and blockchain technologies for cybersecurity in connected vehicles

Ahmad,

Zia,

Naqvi

et al. 2023

WIREs Data Min & Knowl

View full text Add to dashboard Cite

show abstract

“…However, this method does not give the specific interaction process of the protocol, nor does it verify the feasibility of its scheme. Reference [10] uses a neural network method to authenticate messages on the vehicle network, such as the traditional CAN protocol. The author claims that it can effectively improve the accuracy of electronic control unit (ECU) transmission and finally gives analysis and verification, but there was no analysis to assess safety.…”

Section: Related Workmentioning

confidence: 99%

Security Analysis and Improvement of Vehicle Ethernet SOME/IP Protocol

Tang

Feng

2022

Sensors

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The combination of in-vehicle networks and smart car devices has gradually developed into Intelligent Connected Vehicles (ICVs). Through the vehicle security protocol, ICVs can quickly realize communication transmission. However, with the more frequent connections between smart in-vehicle devices and the network, the relationship between intelligent cars and external systems is becoming more and more complicated, and in-vehicle networks are gradually facing many security issues. Strengthening the security of in-vehicle protocols has become particularly important. This paper uses the model building method based on the Colored Petri Net (CPN) theory to model the Scalable service-Oriented MiddlewarE over IP (SOME/IP) protocol of the vehicle Ethernet. The security protocol is formally verified and analyzed by combining it with the Dolev–Yao adversary model detection method. After verification, the protocol is subject to three attack vulnerabilities: replay, tampering, and deception. We introduce timestamps and random numbers to strengthen the protocol security. After the final analysis and verification, the improved scheme in this paper can effectively improve the security performance of the protocol.

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“…Attacks on CAN disrupt the communication between ECUs and the ADAS units to initiate false actuation signals. In [180] a neural network-based model is proposed to authenticate electronic messages sent by ECU through CAN to ensure confidentiality and integrity. It exploits the unique transient response parameters of the CAN channel imposed in ECU signals to generate features that can then differentiate individual ECUs.…”

Section: Resilient Operation Of Self-driving Cars Under Cyber-attacksmentioning

confidence: 99%

Attacks on Self-Driving Cars and Their Countermeasures: A Survey

et al. 2020

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Intelligent Traffic Systems (ITS) are currently evolving in the form of a cooperative ITS or connected vehicles. Both forms use the data communications between Vehicle-to-Vehicle (V2V), Vehicleto-Infrastructure (V2I/I2V) and other on-road entities, and are accelerating the adoption of self-driving cars. The development of cyber-physical systems containing advanced sensors, sub-systems, and smart driving assistance applications over the past decade is equipping unmanned aerial and road vehicles with autonomous decision-making capabilities. The level of autonomy depends upon the make-up and degree of sensor sophistication and the vehicle's operational applications. As a result, self-driving cars are being compromised perceived as a serious threat. Therefore, analyzing the threats and attacks on self-driving cars and ITSs, and their corresponding countermeasures to reduce those threats and attacks are needed. For this reason, some survey papers compiling potential attacks on VANETs, ITSs and self-driving cars, and their detection mechanisms are available in the current literature. However, up to our knowledge, they have not covered the real attacks already happened in self-driving cars. To bridge this research gap, in this paper, we analyze the attacks that already targeted self-driving cars and extensively present potential cyber-attacks and their impacts on those cars along with their vulnerabilities. For recently reported attacks, we describe the possible mitigation strategies taken by the manufacturers and governments. This survey includes recent works on how a self-driving car can ensure resilient operation even under ongoing cyber-attack. We also provide further research directions to improve the security issues associated with self-driving cars.

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ECU Fingerprinting through Parametric Signal Modeling and Artificial Neural Networks for In-vehicle Security against Spoofing Attacks

Cited by 20 publications

References 22 publications

Machine learning and blockchain technologies for cybersecurity in connected vehicles

Machine learning and blockchain technologies for cybersecurity in connected vehicles

Security Analysis and Improvement of Vehicle Ethernet SOME/IP Protocol

Attacks on Self-Driving Cars and Their Countermeasures: A Survey

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