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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

Today, we rely on contactless smart cards to perform several critical operations (e.g., payments and accessing buildings). Attacking smart cards can have severe consequences, such as losing money or leaking sensitive information. Although the security protections embedded in smart cards have evolved over the years, those with weak security properties are still commonly used. Among the different solutions, blocking cards are affordable devices to protect smart cards. These devices are placed close to the smart cards, generating a noisy jamming signal or shielding them. Whereas vendors claim the reliability of their blocking cards, no previous study has ever focused on evaluating their effectiveness. In this paper, we shed light on the security threats on smart cards in the presence of blocking cards, showing the possibility of being bypassed by an attacker. We analyze blocking cards by inspecting their emitted signal and assessing a vulnerability in their internal design. We propose a novel attack that bypasses the jamming signal emitted by a blocking card and reads the content of the smart card. We evaluate the effectiveness of 11 blocking cards when protecting a MIFARE Ultralight smart card and a MIFARE Classic card. Of these 11 cards, we managed to bypass 8 of them and successfully dump the content of a smart card despite the presence of the blocking card. Our findings highlight that the noise type implemented by the blocking cards highly affects the protection level achieved by them. Based on this observation, we propose a countermeasure that may lead to the design of effective blocking cards. To further improve security, we released the tool we developed to inspect the spectrum emitted by blocking cards and set up our attack.

Electric Vehicles (EVs) share common technologies with classical 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 multiple 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 perspective. In this paper, we provide the first in-depth study of the security and privacy threats associated with the EVs ecosystem. We analyze the threats associated with both the EV and the different charging solutions. Focusing on the Cyber-Physical Systems (CPS) paradigm, we provide a detailed analysis of all the processes that an attacker might exploit to affect the security and privacy 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 security and privacy of the EVs ecosystem. Based on our analysis, we stress the need for EV-specific cybersecurity solutions.

Hyperloop is among the most prominent future transportation systems. It involves novel technologies to allow traveling at a maximum speed of 1220km/h while guaranteeing sustainability. Due to the system's performance requirements and the critical infrastructure it represents, its safety and security must be carefully considered. In transportation systems, cyberattacks could lead to safety issues with catastrophic consequences for the population and the surrounding environment. To this day, no research investigated the cybersecurity issues of the Hyperloop technology. In this paper, we provide the first analysis of the cybersecurity challenges of the interconnections between the different components of the Hyperloop ecosystem. We base our analysis on the currently available Hyperloop implementations, distilling those features that will likely be present in its final design. Moreover, we investigate possible infrastructure management approaches and their security concerns. Finally, we discuss countermeasures and future directions for the security of the Hyperloop design., Comment: 9 pages, 4 figures, 1 table

Visible Light Communication (VLC) is one the most promising enabling technology for future 6G networks to overcome Radio-Frequency (RF)-based communication limitations thanks to a broader bandwidth, higher data rate, and greater efficiency. However, from the security perspective, VLCs suffer from all known wireless communication security threats (e.g., eavesdropping and integrity attacks). For this reason, security researchers are proposing innovative Physical Layer Security (PLS) solutions to protect such communication. Among the different solutions, the novel Reflective Intelligent Surface (RIS) technology coupled with VLCs has been successfully demonstrated in recent work to improve the VLC communication capacity. However, to date, the literature still lacks analysis and solutions to show the PLS capability of RIS-based VLC communication. In this paper, we combine watermarking and jamming primitives through the Watermark Blind Physical Layer Security (WBPLSec) algorithm to secure VLC communication at the physical layer. Our solution leverages RIS technology to improve the security properties of the communication. By using an optimization framework, we can calculate RIS phases to maximize the WBPLSec jamming interference schema over a predefined area in the room. In particular, compared to a scenario without RIS, our solution improves the performance in terms of secrecy capacity without any assumption about the adversary's location. We validate through numerical evaluations the positive impact of RIS-aided solution to increase the secrecy capacity of the legitimate jamming receiver in a VLC indoor scenario. Our results show that the introduction of RIS technology extends the area where secure communication occurs and that by increasing the number of RIS elements the outage probability decreases.

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., Comment: 20 pages, 6 figures

EVs (Electric Vehicles) 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. Privacy of users represents one of the possible threats impairing EVs adoption. 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. A suitable feature extractor can hence associate such features to each EV, in what is commonly known as profiling. In this paper, 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 is able to extract features peculiar for each EV, allowing hence for their profiling. We implemented and tested EVScout2.0 over a set of real-world measurements considering over 7500 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 recall and 0.88 precision. To the best of the authors' knowledge, these results set a new benchmark for upcoming privacy research for large datasets of EVs.

The increasing digitization and interconnection of legacy Industrial Control Systems (ICSs) open new vulnerability surfaces, exposing such systems to malicious attackers. Furthermore, since ICSs are often employed in critical infrastructures (e.g., nuclear plants) and manufacturing companies (e.g., chemical industries), attacks can lead to devastating physical damages. In dealing with this security requirement, the research community focuses on developing new security mechanisms such as Intrusion Detection Systems (IDSs), facilitated by leveraging modern machine learning techniques. However, these algorithms require a testing platform and a considerable amount of data to be trained and tested accurately. To satisfy this prerequisite, Academia, Industry, and Government are increasingly proposing testbed (i.e., scaled-down versions of ICSs or simulations) to test the performances of the IDSs. Furthermore, to enable researchers to cross-validate security systems (e.g., security-by-design concepts or anomaly detectors), several datasets have been collected from testbeds and shared with the community. In this paper, we provide a deep and comprehensive overview of ICSs, presenting the architecture design, the employed devices, and the security protocols implemented. We then collect, compare, and describe testbeds and datasets in the literature, highlighting key challenges and design guidelines to keep in mind in the design phases. Furthermore, we enrich our work by reporting the best performing IDS algorithms tested on every dataset to create a baseline in state of the art for this field. Finally, driven by knowledge accumulated during this survey's development, we report advice and good practices on the development, the choice, and the utilization of testbeds, datasets, and IDSs.

The impact of global warming and the imperative to limit climate change have stimulated the need to develop new solutions based on renewable energy sources. One of the emerging trends in this endeavor are the Electric Vehicles (EVs), which use electricity instead of traditional fossil fuels as a power source, relying on the Vehicle-to-Grid (V2G) paradigm. The novelty of such a paradigm requires careful analysis to avoid malicious attempts. An attacker can exploit several surfaces, such as the remote connection between the Distribution Grid and Charging Supply or the authentication system between the charging Supply Equipment and the Electric Vehicles. However, V2G architecture's high cost and complexity in implementation can restrain this field's research capability. In this paper, we approach this limitation by proposing MiniV2G, an open-source emulator to simulate Electric Vehicle Charging (EVC) built on top of Mininet and RiseV2G. MiniV2G is particularly suitable for security researchers to study and test real V2G charging scenarios. MiniV2G can reproduce with high fidelity a V2G architecture to easily simulate an EV charging process. Finally, we present a MiniV2G application and show how MiniV2G can be used to study V2G communication and develop attacks and countermeasures that can be applied to real systems. Since we believe our tool can be of great help for research in this field, we also made it freely available.

Modern Industrial Control Systems (ICSs) allow remote communication through the Internet using industrial protocols that were not designed to work with external networks. To understand security issues related to this practice, prior work usually relies on active scans by researchers or services such as Shodan. While such scans can identify publicly open ports, they cannot identify legitimate use of insecure industrial traffic. In particular, source-based filtering in Network Address Translation or Firewalls prevent detection by active scanning, but do not ensure that insecure communication is not manipulated in transit. In this work, we compare Shodan-only analysis with large-scale traffic analysis at a local Internet Exchange Point (IXP), based on sFlow sampling. This setup allows us to identify ICS endpoints actually exchanging industrial traffic over the Internet. Besides, we are able to detect scanning activities and what other type of traffic is exchanged by the systems (i.e., IT traffic). We find that Shodan only listed less than 2% of hosts that we identified as exchanging industrial traffic, and only 7% of hosts identified by Shodan actually exchange industrial traffic. Therefore, Shodan do not allow to understand the actual use of insecure industrial protocols on the Internet and the current security practices in ICS communications. We show that 75.6% of ICS hosts still rely on unencrypted communications without integrity protection, leaving those critical systems vulnerable to malicious attacks.