Biblio

Increased connectivity increases the attack vector. This also applies to connected vehicles in which vulnerabilities not only threaten digital values but also humans and the environment. Typically, attackers try to exploit the Controller Area Network (CAN) bus, which is the most widely used standard for internal vehicle communication. Once an Electronic Control Unit (ECU) connected to the CAN bus is compromised, attackers can manipulate messages at will. The missing sender authentication by design of the CAN bus enables adversarial access to vehicle functions with severe consequences. In order to address this problem, we propose Scission, an Intrusion Detection System (IDS) which uses fingerprints extracted from CAN frames, enabling the identification of sending ECUs. Scission utilizes physical characteristics from analog values of CAN frames to assess whether it was sent by the legitimate ECU. In addition, to detect comprised ECUs, the proposed system is able to recognize attacks from unmonitored and additional devices. We show that Scission is able to identify the sender with an average probability of 99.85%, during the evaluation on two series production cars and a prototype setup. Due to the robust design of the system, the evaluation shows that all false positives were prevented. Compared to previous approaches, we have significantly reduced hardware costs and increased identification rates, which enables a broad application of this technology.

A key requirement for most security solutions is to provide secure cryptographic key storage in a way that will easily scale in the age of the Internet of Things. In this paper, we focus on providing such a solution based on Physical Unclonable Functions (PUFs). To this end, we focus on microelectromechanical systems (MEMS)-based gyroscopes and show via wafer-level measurements and simulations, that it is feasible to use the physical and electrical properties of these sensors for cryptographic key generation. After identifying the most promising features, we propose a novel quantization scheme to extract bit strings from the MEMS analog measurements. We provide upper and lower bounds for the minimum entropy of the derived bit strings and fully analyze the intra- and inter-class distributions across the operation range of the MEMS device. We complement these measurements via Monte-Carlo simulations based on the distributions of the parameters measured on actual devices. We also propose and evaluate a complete cryptographic key generation chain based on fuzzy extractors. We derive a full entropy 128-bit key using the obtained min-entropy estimates, requiring 1219 bits of helper data with an (authentication) failure probability of 4 . 10-7. In addition, we propose a dedicated MEMS-PUF design, which is superior to our measured sensor, in terms of chip area, quality and quantity of key seed features.