Electric Vehicles (EVs) share common technologies with classic fossil-fueled cars, but they also employ novel technologies and components (e.g., Charging System and Battery Management System) that create an unexplored attack surface for malicious users. Although several contributions in the literature explored cybersecurity aspects of particular components of the EV ecosystem (e.g., charging infrastructure), there is still no contribution to the holistic cybersecurity of EVs and their related technologies from a Cyber-Physical System (CPS) perspective. In this article, we provide the first in-depth study of the Security and Privacy (S&P) threats associated with the EV ecosystem. We analyze the threats associated with both the EV and the different charging solutions. Focusing on the CPS paradigm, we provide a detailed analysis of all the processes that an attacker might exploit to affect the S&P of both drivers and the infrastructure. To address the highlighted threats, we present possible solutions that might be implemented. We also provide an overview of possible future directions to guarantee the S&P of the EV ecosystem. Based on our analysis, we stress the need for EV-specific cybersecurity solutions to help both vehicle owners and infrastructure deployers securing the EV ecosystem.

Common Vulnerability and Exposure (CVE) records are fundamental to cybersecurity, offering unique identifiers for publicly known software and system vulnerabilities. Each CVE is typically assigned a Common Vulnerability Scoring System (CVSS) score to support risk prioritization and remediation. However, score inconsistencies often arise due to subjective interpretations of certain metrics. As the number of new CVEs continues to grow rapidly, automation is increasingly necessary to ensure timely and consistent scoring. While prior studies have explored automated methods, the application of Large Language Models (LLMs), despite their recent popularity, remains relatively underexplored.In this work, we evaluate the effectiveness of LLMs in generating CVSS scores for newly reported vulnerabilities. We investigate various prompt engineering strategies to enhance their accuracy and compare LLM-generated scores against those from embedding-based models, which use vector representations classified via supervised learning. Our results show that while LLMs demonstrate potential in automating CVSS evaluation, embedding-based methods outperform them in scoring more subjective components, particularly confidentiality, integrity, and availability impacts. These findings underscore the complexity of CVSS scoring and suggest that combining LLMs with embedding-based methods could yield more reliable results across all scoring components.

The Controller Area Network (CAN) protocol is the most widely used standard in the automotive industry for in-vehicle networks. However, the CAN protocol lacks essential security features such as encryption and message authentication. Absence of such security features has been shown to make the vehicle network vulnerable to exploits by an adversary. Although multiple types of intrusion detection systems (IDS) have been developed for CAN, it can be difficult to deploy them in real-time with low latency. Further, many of these IDSs are unable to isolate specific CAN frames on which an attack has been mounted, which makes it challenging to design defense mechanisms. In this paper, we develop CANLP, a Natural Language Processing (NLP)-based intrusion detection system to determine whether each transmitted message originated from a legitimate ECU or an adversary. CANLP uses Term Frequency-Inverse Document Frequency (TF-IDF), a NLP technique to discern complex features associated with CAN data and trains machine learning models to identify three types of attacks- fuzzing, spoofing, and masquerade. When an attack is detected, CANLP identifies the malicious CAN frame, which is important for developing resilient systems. Extensive experiments on 4 publicly available vehicle network datasets (which represent data collected from over three vehicle makes and four models) show that CANLP performs attack classification with high F1-scores of 0.9974. We also show that CANLP can be deployed for attack detection on resource-constrained hardware through implementation using RaspberryPi and experiments on a testbed with latency as low as$\lt \text{0.05}~ms$, making it suitable for real-world automotive applications such as fleet monitoring within the same vehicle class.

Recent advancements in Artificial Intelligence, and particularly Large Language Models (LLMs), offer promising prospects for aiding system administrators in managing the complexity of modern networks. However, despite this potential, a significant gap exists in the literature regarding the extent to which LLMs can understand computer networks. Without empirical evidence, system administrators might rely on these models without assurance of their efficacy in performing network-related tasks accurately.In this paper, we are the first to conduct an exhaustive study on LLMs’ comprehension of computer networks. We formulate several research questions to determine whether LLMs can provide correct answers when supplied with a network topology and questions on it. To assess them, we developed a thorough framework for evaluating LLMs’ capabilities in various network-related tasks. We evaluate our framework on multiple computer networks employing proprietary (e.g., GPT4) and open-source (e.g., Llama2) models. Our findings in general purpose LLMs using a zero-shot scenario demonstrate promising results, with the best model achieving an average accuracy of 79.3%. Proprietary LLMs achieve noteworthy results in small and medium networks, while challenges persist in comprehending complex network topologies, particularly for open-source models. Moreover, we provide insight into how prompt engineering can enhance the accuracy of some tasks.

Electric Vehicles (EVs) represent a green alternative to traditional fuel-powered vehicles. To enforce their widespread use, both the technical development and the security of users shall be guaranteed. Users’ privacy represents a possible threat that impairs the adoption of EVs. In particular, recent works showed the feasibility of identifying EVs based on the current exchanged during the charging phase. In fact, while the resource negotiation phase runs over secure communication protocols, the signal exchanged during the actual charging contains features peculiar to each EV. In what is commonly known as profiling, a suitable feature extractor can associate such features to each EV. In this article, we propose EVScout2.0 , an extended and improved version of our previously proposed framework to profile EVs based on their charging behavior. By exploiting the current and pilot signals exchanged during the charging phase, our scheme can extract features peculiar for each EV, hence allowing their profiling. We implemented and tested EVScout2.0 over a set of real-world measurements considering over 7,500 charging sessions from a total of 137 EVs. In particular, numerical results show the superiority of EVScout2.0 with respect to the previous version. EVScout2.0 can profile EVs, attaining a maximum of 0.88 for both recall and precision scores in the case of a balanced dataset. To the best of the authors’ knowledge, these results set a new benchmark for upcoming privacy research for large datasets of EVs.

Abstract To support the increasing spread of Electric Vehicles (EVs), Charging Stations (CSs) are being installed worldwide. The new generation of CSs employs the Vehicle-To-Grid (V2G) paradigm by implementing novel standards such as the ISO 15118. This standard enables high-level communication between the vehicle and the charging column, helps manage the charge smartly, and simplifies the payment phase. This novel charging paradigm, which connects the Smart Grid to external networks (e.g., EVs and CSs), has not been thoroughly examined yet. Therefore, it may lead to dangerous vulnerability surfaces and new research challenges. In this paper, we present EVExchange , the first attack to steal energy during a charging session in a V2G communication: i.e., charging the attacker’s car while letting the victim pay for it. Furthermore, if reverse charging flow is enabled, the attacker can even sell the energy available on the victim’s car! Thus, getting the economic profit of this selling, and leaving the victim with a completely discharged battery. We developed a virtual and a physical testbed in which we validate the attack and prove its effectiveness in stealing the energy. To prevent the attack, we propose a lightweight modification of the ISO 15118 protocol to include a distance bounding algorithm. Finally, we validated the countermeasure on our testbeds. Our results show that the proposed countermeasure can identify all the relay attack attempts while being transparent to the user.