The Controller Area Network (CAN), which is used for communication between in-vehicle devices, has been shown to be vulnerable to spoofing attacks. Voltage-based spoofing detection (VBS-D) mechanisms are considered state-of-the-art solutions, complementing cryptography-based authentication whose security is limited due to the CAN protocol’s limited message size. Unfortunately, VBS-D mechanisms are vulnerable to poisoning performed by a malicious device connected to the CAN bus, specifically designed to poison the deployed VBS-D mechanism as it adapts to environmental changes that take place when the vehicle is moving. In this paper, we harden VBS-D mechanisms using a deep learning-based mechanism which runs immediately, when the vehicle starts; this mechanism utilizes physical side-channels to detect and locate physical intrusions, even when the malicious devices connected to the CAN bus are silent. We demonstrate the mechanism’s effectiveness (100% intrusion detection accuracy and error rates of close to 0%) in various physical intrusion scenarios and varying temperatures on a CAN bus prototype. In addition, we present a deep learning-based VBS-D mechanism that securely adapts to environmental changes. This mechanism’s robustness (99.8% device identification accuracy) is demonstrated on a real moving vehicle.

Fingerprinting smartphones based on acoustic characteristics of their loudspeaker may have a number of applications in device-to-device authentication as well as in forensic investigations. In this work we propose an efficient fingerprinting methodology by using the roll-off characteristics of the device speaker, i.e., the transition between the low and high stopbands to the passband segment of the speaker. We extract roll-off characteristics from sweep signals, also know as chirps, that are commonly used in practice to test speaker response. This procedure appears to be more stable against variations of the volume level and allows the use of simple linear approximations, which are intuitive and easy to compute, in order to extract the fingerprint. To increase detection accuracy, on the basis of the proven performance of deep learning techniques, a convolutional and a bi-directional long short term memory neural network are further proposed and their performance demonstrated for authentication purposes. While numerous applications may be envisioned, we specifically focus on the use of speaker characteristics in relation to in-vehicle infotainment units, checking if recordings from these units can be used to fingerprint a specific phone.