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Despite its well-known security issues, the Controller Area Network (CAN) is still the main technology for in-vehicle communications. Attackers posing as diagnostic services or accessing the CAN bus can threaten the drivers' location privacy to know the exact location at a certain point in time or to infer the visited areas. This represents a serious threat to users' privacy, but also an advantage for police investigations to gather location-based evidence. In this paper, we present On Path Diagnostic - Intrusion \& Inference (OPD-II), a novel path inference attack leveraging a physical car model and a map matching algorithm to infer the path driven by a car based on CAN bus data. Differently from available attacks, our approach only requires the attacker to know the initial location and heading of the victim's car and is not limited by the availability of training data, road configurations, or the need to access other victim's devices (e.g., smartphones). We implement our attack on a set of four different cars and a total number of 41 tracks in different road and traffic scenarios. We achieve an average of 95% accuracy on reconstructing the coordinates of the recorded path by leveraging a dynamic map-matching algorithm that outperforms the 75% and 89% accuracy values of other proposals while removing their set of assumptions.

Dynamic Wireless Power Transfer (DWPT) is a novel technology that allows charging an electric vehicle while driving thanks to a dedicated road infrastructure. DWPT's capabilities in automatically establishing charging sessions and billing without users' intervention make it prone to cybersecurity attacks. Hence, security is essential in preventing fraud, impersonation, and user tracking. To this aim, researchers proposed different solutions for authenticating users. However, recent advancements in quantum computing jeopardize classical public key cryptography, making currently existing solutions in DWPT authentication nonviable. To avoid the resource burden imposed by technology upgrades, it is essential to develop post-quantum-resistant solutions. In this paper, we propose DynamiQS, the first post-quantum secure authentication protocol for dynamic wireless charging. DynamiQS is privacy-preserving and secure against attacks on the DWPT. We leverage an Identity-Based Encryption with Lattices in the Ring Learning With Error framework. Furthermore, we show the possibility of using DynamiQS in a real environment, leveraging the results of cryptographic computation on real constrained devices and simulations. DynamiQS reaches a total time cost of around 281 ms, which is practicable in dynamic charging settings (car and charging infrastructure).

To fight climate change, new "green" technology are emerging, most of them using electricity as a power source. Among the solutions, Electric Vehicles (EVs) represent a central asset in the future transport system. EVs require a complex infrastructure to enable the so-called Vehicle-to-Grid (V2G) paradigm to manage the charging process between the smart grid and the EV. In this paradigm, the Electric Vehicle Supply Equipment (EVSE), or charging station, is the end device that authenticates the vehicle and delivers the power to charge it. However, since an EVSE is publicly exposed and connected to the Internet, recent works show how an attacker with physical tampering and remote access can target an EVSE, exposing the security of the entire infrastructure and the final user. For this reason, it is important to develop novel strategies to secure such infrastructures. In this paper we present HoneyEVSE, the first honeypot conceived to simulate an EVSE. HoneyEVSE can simulate with high fidelity the EV charging process and, at the same time, enables a user to interact with it through a dashboard. Furthermore, based on other charging columns exposed on the Internet, we emulate the login and device information pages to increase user engagement. We exposed HoneyEVSE for 30 days to the Internet to assess its capability and measured the interaction received with its Shodan Honeyscore. Results show that HoneyEVSE can successfully evade the Shodan honeyscore metric while attracting a high number of interactions on the exposed services., Comment: 15 pages

Dynamic wireless power transfer provides means for charging Electric Vehicles (EVs) while driving, avoiding stopping for charging and hence fostering their widespread adoption. Researchers devoted much effort over the last decade to provide a reliable infrastructure for potential users to improve comfort and time management. Due to the severe security and performance system requirements, the different scheme proposed in last years lack of 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 for the communication and reducing security due to the large number of message exchanges. In this paper, we propose a secure, computationally lightweight, unified protocol for fast authentication and billing that provides on-demand dynamic charging to comprehensively deal with all the computational and security constraints. 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 overcomes the problem of overwhelming interactions and provides public scheme security against the use of simple operations in wide open communications without impacting on performance., Comment: 13 pages

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., Comment: 6 pages, conference

In the context of multi-player, general-sum games, there is an increasing interest in solution concepts modeling some form of communication among players, since they can lead to socially better outcomes with respect to Nash equilibria, and may be reached through learning dynamics in a decentralized fashion. In this paper, we focus on coarse correlated equilibria (CCEs) in sequential games. First, we complete the picture on the complexity of finding social-welfare-maximizing CCEs by showing that the problem is not in Poly-APX unless P = NP. Furthermore, simple arguments show that CFR - working with behavioral strategies - may not converge to a CCE. However, we devise a simple variant (CFR-S) which provably converges to the set of CCEs, but may be empirically inefficient. Thus, we design a variant of the CFR algorithm (called CFR-Jr) which approaches the set of CCEs with a regret bound sub-linear in the size of the game, and is shown to be dramatically faster than CFR-S and the state-of-the-art algorithms to compute CCEs.

Coarse correlation models strategic interactions of rational agents complemented by a correlation device, that is a mediator that can recommend behavior but not enforce it. Despite being a classical concept in the theory of normal-form games for more than forty years, not much is known about the merits of coarse correlation in extensive-form settings. In this paper, we consider two instantiations of the idea of coarse correlation in extensive-form games: normal-form coarse-correlated equilibrium (NFCCE), already defined in the literature, and extensive-form coarse-correlated equilibrium (EFCCE), which we introduce for the first time. We show that EFCCE is a subset of NFCCE and a superset of the related extensive-form correlated equilibrium. We also show that, in two-player extensive-form games, social-welfare-maximizing EFCCEs and NFCEEs are bilinear saddle points, and give new efficient algorithms for the special case of games with no chance moves. In our experiments, our proposed algorithm for NFCCE is two to four orders of magnitude faster than the prior state of the art.