Frank L. Combs

Miki E. Verma, Robert A. Bridges, Michael D. Iannacone et al.

Although ubiquitous in modern vehicles, Controller Area Networks (CANs) lack basic security properties and are easily exploitable. A rapidly growing field of CAN security research has emerged that seeks to detect intrusions on CANs. Producing vehicular CAN data with a variety of intrusions is out of reach for most researchers as it requires expensive assets and expertise. To assist researchers, we present the first comprehensive guide to the existing open CAN intrusion datasets, including a quality analysis of each dataset and an enumeration of each's benefits, drawbacks, and suggested use case. Current public CAN IDS datasets are limited to real fabrication (simple message injection) attacks and simulated attacks often in synthetic data, which lack fidelity. In general, the physical effects of attacks on the vehicle are not verified in the available datasets. Only one dataset provides signal-translated data but not a corresponding raw binary version. Overall, the available data pigeon-holes CAN IDS works into testing on limited, often inappropriate data (usually with attacks that are too easily detectable to truly test the method), and this lack data has stymied comparability and reproducibility of results. As our primary contribution, we present the ROAD (Real ORNL Automotive Dynamometer) CAN Intrusion Dataset, consisting of over 3.5 hours of one vehicle's CAN data. ROAD contains ambient data recorded during a diverse set of activities, and attacks of increasing stealth with multiple variants and instances of real fuzzing, fabrication, and unique advanced attacks, as well as simulated masquerade attacks. To facilitate benchmarking CAN IDS methods that require signal-translated inputs, we also provide the signal time series format for many of the CAN captures. Our contributions aim to facilitate appropriate benchmarking and needed comparability in the CAN IDS field.

Krzysztof Pawelec, Robert A. Bridges, Frank L. Combs

Modern vehicles contain a few controller area networks (CANs), which allow scores of on-board electronic control units (ECUs) to communicate messages critical to vehicle functions and driver safety. CAN provide a lightweight and reliable broadcast protocol but is bereft of security features. As evidenced by many recent research works, CAN exploits are possible both remotely and with direct access, fueling a growing CAN intrusion detection system (IDS) body of research. A challenge for pioneering vehicle-agnostic IDSs is that passenger vehicles' CAN message encodings are proprietary, defined and held secret by original equipment manufacturers (OEMs). Targeting detection of next-generation attacks, in which messages are sent from the expected ECU at the expected time but with malicious content, researchers are now seeking to leverage "CAN data models", which predict future CAN message contents and use prediction error to identify anomalous, hopefully malicious CAN messages. Yet, current works model CAN signals post-translation, i.e., after applying OEM-donated or reverse-engineered translations from raw data. In this work, we present initial IDS results testing deep neural networks used to predict CAN data at the bit level, thereby providing IDS capabilities but avoiding reverse engineering proprietary encodings. Our results suggest the method is promising for continuous signals in CAN data, but struggles for discrete, e.g., binary, signals.

Zachariah Tyree, Robert A. Bridges, Frank L. Combs et al.

Modern vehicles rely on scores of electronic control units (ECUs) broadcasting messages over a few controller area networks (CANs). Bereft of security features, in-vehicle CANs are exposed to cyber manipulation and multiple researches have proved viable, life-threatening cyber attacks. Complicating the issue, CAN messages lack a common mapping of functions to commands, so packets are observable but not easily decipherable. We present a transformational approach to CAN IDS that exploits the geometric properties of CAN data to inform two novel detectors--one based on distance from a learned, lower dimensional manifold and the other on discontinuities of the manifold over time. Proof-of-concept tests are presented by implementing a potential attack approach on a driving vehicle. The initial results suggest that (1) the first detector requires additional refinement but does hold promise; (2) the second detector gives a clear, strong indicator of the attack; and (3) the algorithms keep pace with high-speed CAN messages. As our approach is data-driven it provides a vehicle-agnostic IDS that eliminates the need to reverse engineer CAN messages and can be ported to an after-market plugin.