We address the secure pairing of mobile devices based on accelerometer data under various transportation environments, e.g., train, tram, car, bike, walking, etc. As users commonly commute by several transportation modes, extracting session keys from various scenarios to secure the private network of user's devices or even the public network formed by devices belonging to distinct users that share the same location is crucial. The main goal of our work is to establish the amount of entropy that can be collected from these environments in order to determine concrete security bounds for each environment. We test several signal processing techniques on the extracted data, e.g., low-pass and high-pass filters, then apply sigma-delta modulation in order to expand the size of the feature vectors and increase both the pairing success rate and security level. Further, we bootstrap secure session keys by the use of existing cryptographic building blocks EKE (Encrypted Key Exchange) and SPEKE (Simple Password Exponential Key Exchange). We implement our proof-of-concept application on Android smart-phones and take benefit from numerical processing environments for the off-line analysis of the collected datasets.
Smartphones are quickly moving toward complementing or even replacing traditional car keys. We advocate a role-based access control policy mixed with attributes that facilitates access to various functionalities of vehicular on-board units from smartphones. We use a rights-based access control policy for in-vehicle functionalities similar to the case of a file allocation table of a contemporary OS, in which read, write or execute operations can be performed over various vehicle functions. Further, to assure the appropriate security, we develop a protocol suite using identity-based cryptography and we rely on group signatures which preserve the anonymity of group members thus assuring privacy and traceability. To prove the feasibility of our approach, we develop a proof-of-concept implementation with modern smartphones, aftermarket Android head-units and test computational feasibility on a real-world in-vehicle controller. Our implementation relies on state-of-the-art cryptography, including traditional building blocks and more modern pairing-friendly curves, which facilitate the adoption of group signatures and identitybased cryptography in automotive-based scenarios.
Digital twins are used to replicate the behavior of physical systems, and in-vehicle networks can greatly benefit from this technology. This is mainly because in-vehicle networks circulate large amounts of data coming from various sources such as wired, or in some cases even wireless, sensors that are fused by actuators responsible for safety-critical tasks that require careful testing. In this work, we build a laboratory in-vehicle network that mimics a real vehicle network in regards to wire length, number of stubs and devices that are connected to it. The Controller Area Network (CAN), which is still the most popular communication bus inside cars, is used as a network layer. Using models defined in MATLAB for various subsystems, e.g., Anti-lock Braking System (ABS), Powertrain and Electric Power-Steering, deployed on automotive-grade microcontrollers, we evaluate the in-vehicle bus digital twin by providing realistic inputs and recording and reproducing in-vehicle network traffic. The experimental results showed good correlation between the output of the implemented digital twin and the data collected from an actual car.
Camera sensor identification can have numerous forensics and authentication applications. In this work, we follow an identification methodology for smartphone camera sensors using properties of the Dark Signal Nonuniformity (DSNU) in the collected images. This requires taking dark pictures, which the users can easily do by keeping the phone against their palm, and has already been proposed by various works. From such pictures, we extract low and mid frequency AC coefficients from the DCT (Discrete Cosine Transform) and classify the data with the help of machine learning techniques. Traditional algorithms such as KNN (K-Nearest Neighbor) give reasonable results in the classification, but we obtain the best results with a wide neural network, which, despite its simplicity, surpassed even a more complex network architecture that we tried. Our analysis showed that the blue channel provided the best separation, which is in contrast to previous works that have recommended the green channel for its higher encoding power.
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