Title | AI Based Algorithm-Hardware Separation for IoV Security |
Publication Type | Conference Paper |
Year of Publication | 2021 |
Authors | Aman, Muhammad Naveed, Sikdar, Biplab |
Conference Name | 2021 IEEE Globecom Workshops (GC Wkshps) |
Keywords | algorithm-hardware separation, cyber physical systems, Hardware, Human Behavior, human factors, Inference algorithms, Internet of Vehicles, internet of vehicles security, machine learning algorithms, Market research, Metrics, pubcrawl, resilience, Resiliency, Roads, Routing protocols, security, vehicular ad hoc networks |
Abstract | The Internet of vehicles is emerging as an exciting application to improve safety and providing better services in the form of active road signs, pay-as-you-go insurance, electronic toll, and fleet management. Internet connected vehicles are exposed to new attack vectors in the form of cyber threats and with the increasing trend of cyber attacks, the success of autonomous vehicles depends on their security. Existing techniques for IoV security are based on the un-realistic assumption that all the vehicles are equipped with the same hardware (at least in terms of computational capabilities). However, the hardware platforms used by various vehicle manufacturers are highly heterogeneous. Therefore, a security protocol designed for IoVs should be able to detect the computational capabilities of the underlying platform and adjust the security primitives accordingly. To solve this issue, this paper presents a technique for algorithm-hardware separation for IoV security. The proposed technique uses an iterative routine and the corresponding execution time to detect the computational capabilities of a hardware platform using an artificial intelligence based inference engine. The results on three different commonly used micro-controllers show that the proposed technique can effectively detect the type of hardware platform with up to 100% accuracy. |
DOI | 10.1109/GCWkshps52748.2021.9681992 |
Citation Key | aman_ai_2021 |