Dynamic wireless power transfer provides a means for charging Electric Vehicles (EVs) while driving, avoiding stopping to charge and hence fostering their widespread adoption. Researchers have devoted much effort over the last decade to providing a reliable infrastructure for potential users to improve their comfort and time management. Due to the severe security and performance system requirements, the different schemes proposed in the last years lack a unified protocol involving the modern architecture model with merged authentication and billing processes. Furthermore, they require the continuous interaction of the trusted entity during the process, increasing the delay in communication and reducing security due to a large number of message exchanges. This article proposes a secure, computationally lightweight, unified protocol for fast authentication and billing that provides on-demand dynamic charging to deal with all the computational and security comprehensively with additional usability for the customers. The protocol employs an ID-based public encryption scheme to manage mutual authentication and pseudonyms to preserve the user's identity across multiple charging processes. Compared to state-of-the-art authentication protocols, our proposal provides on-demand service and public critical infrastructure security without impacting performances with around 7 ms, close to the most straightforward scheme available.

Transfer learning (TL) has been demonstrated to improve DNN model performance when faced with a scarcity of training samples. However, the suitability of TL as a solution to reduce vulnerability of overfitted DNNs to privacy attacks is unexplored. A class of privacy attacks called membership inference attacks (MIAs) aim to determine whether a given sample belongs to the training dataset (member) or not (nonmember). We introduce Double-Dip to investigate the use of TL (Stage-1) combined with randomization (Stage-2) to thwart MIAs on overfitted DNNs without degrading classification accuracy. Our study examines roles of shared feature space and parameter values between source and target models, number of frozen layers, and complexity of pretrained models. Our preliminary evaluations of Double-Dip demonstrate that Stage-1 reduces adversary success while also significantly increasing classification accuracy of nonmembers against an adversary attempting to carry out SOTA label-only MIAs. After Stage-2, success of an adversary carrying out a label-only MIA is further reduced to near 50%, bringing it closer to a random guess and showing the effectiveness of Double-Dip. Stage-2 of Double-Dip also achieves lower ASR and higher classification accuracy than regularization and differential privacy-based methods.

Machine learning models that use deep neural networks (DNNs) are vulnerable to backdoor attacks. An adversary carrying out a backdoor attack embeds a predefined perturbation called a trigger into a small subset of input samples and trains the DNN such that the presence of the trigger in the input results in an adversary-desired output class. Such adversarial retraining however needs to ensure that outputs for inputs without the trigger remain unaffected and provide high classification accuracy on clean samples. Existing defenses against backdoor attacks are computationally expensive, and their success has been demonstrated primarily on image-based inputs. The increasing popularity of deploying pretrained DNNs to reduce costs of re/training large models makes defense mechanisms that aim to detect 'suspicious' input samples preferable.

Dynamic Wireless Power Transfer (DWPT) can be used for on-demand recharging of Electric Vehicles (EV) while driving. However, DWPT raises numerous security and privacy concerns. Recently, researchers demonstrated that DWPT systems are vulnerable to adversarial attacks. In an EV charging scenario, an attacker can prevent the authorized customer from charging, obtain a free charge by billing a victim user and track a target vehicle. State-of-the-art authentication schemes relying on centralized solutions are either vulnerable to various attacks or have high computational complexity, making them unsuitable for a dynamic scenario. In this paper, we propose Quick Electric Vehicle SEcure Charging (QEVSEC), a novel, secure, and efficient authentication protocol for the dynamic charging of EVs. Our idea for QEVSEC originates from multiple vulnerabilities we found in the state-of-the-art protocol that allows tracking of user activity and is susceptible to replay attacks. Based on these observations, the proposed protocol solves these issues and achieves lower computational complexity by using only primitive cryptographic operations in a very short message exchange. QEVSEC provides scalability and a reduced cost in each iteration, thus lowering the impact on the power needed from the grid.