As the security vulnerabilities of controller area networks (CAN) become well known, heavy-duty vehicles implementing the SAE J1939 specification layer on this bus are immediate targets. Recently released standards provide clear cybersecurity requirements, but the exact methods to be implemented are not specified and remain up to the manufacturers. In this work, we address adversary actions and countermeasures at the control system level for a heavy-duty vehicle J1939 CAN bus. This low level approach allows us to complement regular attacks with more knowledgeable attacks that may evade detection and discuss realistic countermeasures. Indeed, as we also show by experiments, traditional approaches based on machine learning algorithms will largely fail to detect such attacks. We present experiments based on a model that links between the Simulink environment, an extension of the MATLAB platform for the simulation of in-vehicle control systems, with the CANoe environment, which facilitates the simulation of in-vehicle networks.

In the recent years, there has been a lot of focus on designing security for in-vehicle networks and detecting intrusions. Still, no countermeasure is perfect and most of the existing intrusion detection systems have a nonzero false negative rate, which implies that adversarial frames may still go undetected on the bus. Unfortunately, answers are largely missing for what will happen with the vehicle in such circumstances, i.e., how is the safety of the vehicle and bystanders affected by adversarial actions that go undetected, while there are little or no answers on the acceptable misclassification rates in real-world deployments. In this article, we attempt to provide such answers by pursuing an impact assessment for adversarial actions on the bus assuming low false negative rates. The assessment is based on the effects of such attacks on models for automatic emergency braking and adaptive cruise control systems that are implemented in Simulink, a commonly used tool for designing such systems in the automotive industry. To achieve this, we embed adversarial behavior into the Simulink model, according to recently reported attacks on in-vehicle controller area network buses. This allows us to assess the impact of adversarial actions according to existing safety standards and regulations.

Smartphones are a vital technology, they improve our social interactions, provide us a great deal of information and bring forth the means to control various emerging technologies, like the numerous IoT devices that are controlled via smartphone apps. In this context, smartphone fingerprinting from sensor characteristics is a topic of high interest not only due to privacy implications or potential use in forensics investigations, but also because of various applications in device authentication. In this work we review existing approaches for smartphone fingerprinting based on internal components, focusing mostly on camera sensors, microphones, loudspeakers and accelerometers. Other sensors, i.e., gyroscopes and magnetometers, are also accounted, but they correspond to a smaller body of works. The output of these transducers, which convert one type of energy into another, e.g., mechanical into electrical, leaks through various channels such as mobile apps and cloud services, while there is little user awareness on the privacy risks. Needless to say, miniature physical imperfections from the manufacturing process make each such transducer unique. One of the main intentions of our study is to rank these sensors according to the accuracy they provide in identifying smartphones and to give a clear overview on the amount of research that each of these components triggered so far. We review the features which can be extracted from each type of data and the classification algorithms that have been used. Last but not least, we also point out publicly available datasets which can serve for future investigations.

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.

As car sharing becomes an increasingly common task, mediating user access rights from external servers comes with threats regarding user’s privacy. Clearly, users can be tracked by service mediators, e.g., cloud providers, that manage vehicle fleets, etc. In this work we design and test a simple solution based on oblivious transfer, a well-known and secure cryptographic block, that allows to preserve user’s privacy when gaining access to the vehicle. We test the feasibility of deploying such a solution on Android capable smartphones but also account for potential in-vehicle components, e.g., car head units, that may be soon put to such tasks. We use Microsoft Azure as cloud service provider and deploy a Java implementation, based on the Bouncy Castle cryptographic library, on the server side. Our experimental results show that Android based units are capable of handling the required cryptographic operations and the implementation of the employed protocol can be done by existing open-source support.