What standardization is needed to ensure that quantum technologies do not pose an unacceptable risk from an automotive cybersecurity perspective? Click here to access the full SAE EDGETM Research Report portfolio.
While current cybersecurity endeavors in the heavy-duty (HD) vehicle space focus on securing conventional communication technologies such as the controller area network (CAN), there is a notable deficiency in defensive research concerning legacy technologies, particularly those utilized between trucks and trailers. ...To the best of current knowledge, this publication marks the first presentation of cybersecurity defense research on the SAE J1708/J1587 protocol stack.
Using a wireless medium for tractor-trailer communication will bring new cybersecurity challenges and requirements which requires new development and lifecycle considerations.
The article also focus on innovative approaches that have recently adopted my many cybersecurity professionals for secured operation of ITS involving block-chain, artificial intelligence, and Machine Learning.
Unmanned ground vehicles (UGVs) may encounter difficulties accommodating environmental uncertainties and system degradations during harsh conditions. However, human experience and onboard intelligence can may help mitigate such cases. Unfortunately, human operators have cognition limits when directly supervising multiple UGVs. Ideally, an automated decision aid can be designed that empowers the human operator to supervise the UGVs. In this paper, we consider a connected UGV platoon under cyber attacks that may disrupt safety and degrade performance. An observer-based resilient control strategy is designed to mitigate the effects of vehicle-to-vehicle (V2V) cyber attacks. In addition, each UGV generates both internal and external evaluations based on the platoons performance metrics. A cloud-based trust-based information management system collects these evaluations to detect abnormal UGV platoon behaviors.
Multiple approaches have been created to enhance intra-vehicle communications security over the past three decades since the introduction of the Controller Area Network (CAN) protocol. The twin pair differential-mode communications bus is tremendously robust in the face of interference, yet physical access to the bus offers a variety of potential attack vectors whereby false messages and/or denial of service are achievable. This paper evaluates extensions of a Physical-layer (PHY) common-mode watermark-based authentication technique recently developed to improve authentication on the CAN bus by considering the watermark as a side-channel communications means for high value information. We also propose and analyze higher layer algorithms, with benefits and pitfalls, for employing the watermark as a physical-layer firewall.
To help address the issue of message authentication on the Controller Area Network (CAN) bus, researchers at Virginia Tech and Ford Motor Company have developed a proof-of-concept time-evolving watermark-based authentication mechanism that offers robust, cryptographically controlled confirmation of a CAN message's authenticity. This watermark is injected as a common-mode signal on both CAN-HI and CAN-LO bus voltages and has been proven using a low-cost software-defined radio (SDR) testbed. This paper extends prior analysis on the design and proof-of-concept to consider robustness testing over the range of voltages, both steady state drifts and transients, as are commonly witnessed within a vehicle. Overall performance results, along with a dynamic watermark amplitude control, validate the concept as being a practical near-term approach at improving authentication confidence of messages on the CAN bus.
Autonomous vehicles might one day be able to implement privacy preserving driving patterns which humans may find too difficult to implement. In order to measure the difference between location privacy achieved by humans versus location privacy achieved by autonomous vehicles, this paper measures privacy as trajectory anonymity, as opposed to single location privacy or continuous privacy. This paper evaluates how trajectory privacy for randomized driving patterns could be twice as effective for autonomous vehicles using diverted paths compared to Google Map API generated shortest paths. The result shows vehicles mobility patterns could impact trajectory and location privacy. Moreover, the results show that the proposed metric outperforms both K-anonymity and KDT-anonymity.