Cybersecurity Risk Analysis of Electric Vehicles Charging Stations

Hamdare, Safa; Kaiwartya, Omprakash; Aljaidi, Mohammad; Jugran, Manish; Cao, Yue; Kumar, Sushil; Mahmud, Mufti; Brown, David; Lloret, Jaime

doi:10.3390/s23156716

Cited by 45 publications

(5 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The exploration of cybersecurity in EVCS encompasses various facets, with research focusing on several key areas. First, comprehensive cybersecurity risk analyses [17], investigations into vulnerabilities induced by manufacturers [18], and post-cyber event investigation frameworks [19] collectively address the broad security challenges in EVCS. These studies delve into infrastructure and protocol vulnerabilities, underscore the need for enhanced security measures, and introduce sophisticated frameworks for post-event analysis.…”

Section: Related Workmentioning

confidence: 99%

Next–Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Models

Kilichev,

Turimov,

Kim

2024

Mathematics

View full text Add to dashboard Cite

In the evolving landscape of Internet of Things (IoT) and Industrial IoT (IIoT) security, novel and efficient intrusion detection systems (IDSs) are paramount. In this article, we present a groundbreaking approach to intrusion detection for IoT-based electric vehicle charging stations (EVCS), integrating the robust capabilities of convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU) models. The proposed framework leverages a comprehensive real-world cybersecurity dataset, specifically tailored for IoT and IIoT applications, to address the intricate challenges faced by IoT-based EVCS. We conducted extensive testing in both binary and multiclass scenarios. The results are remarkable, demonstrating a perfect 100% accuracy in binary classification, an impressive 97.44% accuracy in six-class classification, and 96.90% accuracy in fifteen-class classification, setting new benchmarks in the field. These achievements underscore the efficacy of the CNN-LSTM-GRU ensemble architecture in creating a resilient and adaptive IDS for IoT infrastructures. The ensemble algorithm, accessible via GitHub, represents a significant stride in fortifying IoT-based EVCS against a diverse array of cybersecurity threats.

show abstract

Section: Related Workmentioning

confidence: 99%

Next–Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Models

Kilichev,

Turimov,

Kim

2024

Mathematics

View full text Add to dashboard Cite

show abstract

“…The communication equipment (ethernet and Wi-Fi) used in V2G technologies, allows hackers to access and affect the entire charging network through a single charger [57]. By infiltrating the EVCS, attackers can easily manipulate targeted critical components, of the MG which can have significant implications, including power disruptions and threats to public safety [58].…”

Section: ) Cybersecurity Risksmentioning

confidence: 99%

Enhancing Microgrid Operation Through Electric Vehicle Integration: A Survey

Sora,

Serban,

Petreus

2024

IEEE Access

View full text Add to dashboard Cite

The integration of renewable energy sources (RES) and electric vehicles (EVs) into microgrids (MGs) has significant potential for enhancing energy resilience, addressing environmental concerns, and promoting decentralized energy systems as a global shift towards sustainable energy solutions. Therefore, this survey paper provides a comprehensive discussion on improving MG operation through EV integration. This study evaluates the status of EV integration into MGs, focusing on technological advancements, and emerging trends, while pinpointing key technical challenges and opportunities. Furthermore, this paper examines the pivotal role of EVs in participating in vehicle-to-grid (V2G) services, providing ancillary support to improve MG performance. The importance of a reliable communication infrastructure for information exchange between EV, EV charging stations (EVCSs), and MG has been emphasized for the effective implementation of V2G services. This discussion extends to the contributions of EVs to primary, secondary, and tertiary MG controls. The paper also analyzes the integration of EVs into AC and DC MGs and further proposes configurations for both MG cases. Finally, the paper concludes by providing recommendations for future research to unlock the full potential of EV contributions to MG performance, thereby contributing to the ongoing advancement of sustainable and resilient energy systems. The key findings of this work include solutions for MG voltage and frequency regulation implemented through EV bidirectional converter power flow control, EV charger configurations for integration into AC and DC, EV contributions in improving the MG's operational resilience and adaptability, and the noteworthy challenges arising from V2G implementation in such systems.INDEX TERMS electric vehicle charging, microgrids, renewable energy sources, vehicle-to-grid

show abstract

“…Several proposals in the literature have addressed the area of EVs and their related research problems. Mostly, they address issues to safety, power consumption, battery manufacturing technologies, and cybersecurity risks [5]. When it comes to charging, most of the research focuses on power grids, load-balancing, wired/ wireless charging techniques [6], and scheduling from both economic and load perspectives [7], [8], [9].…”

Section: Related Workmentioning

confidence: 99%

AFARM: Anxiety-Free Autonomous Routing Model for Electric Vehicles with Dynamic Route Preferences

Quttoum,

Alsarhan,

Moh'd

et al. 2024

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

Energy and environmental concerns have fostered the era of electric vehicles (EVs) to take over and be welcomed more than ever. Fuel-powered vehicles are still predominant; however, this trend appears to be changing sooner than we might expect. Countries in Europe, Asia, and many states in America have already made the decision to transition to a fully EV industry in the next few years. This looks promising; however, drivers still have concerns about the battery mileage of such vehicles and the anxiety that such driving experiences! Indeed, driving with the probability of having insufficient battery charge that may be involved in guaranteeing the delivery to the trip destination imposes a level of anxiety on the vehicle drivers. Therefore, for an alternative to traditional fuel-powered vehicles to be convincing, there needs to be sufficient coverage of charging stations to serve cities in the same way that fuel stations serve traditional vehicles. The current navigation models select routes based solely on distance and traffic metrics, without taking into account the coverage of fuel service stations that these routes may offer. This assumption is made under the belief that all routes are adequately covered. This might be true for fuel-powered vehicles, but not for EVs. Hence, in this work, we are presenting AFARM, a routing model that enables a smart navigation system specifically designed for EVs. This model routes the EVs via paths that are lined with charging stations that align with the EV’s current charge requirements. Different from the other models proposed in the literature, AFARM is autonomous in the sense that it determines navigation paths for each vehicle based on its make, model, and current battery status. Moreover, it employs Dijkstra’s algorithm to accommodate varying least-cost navigation preferences, ranging from shortest-distance routes to routes with the shortest trip time and routes with maximum residual battery capacities as well. According to the EV driver’s preference, AFARM checks the set of candidate paths at the source point and selects the appropriate path for the vehicle to drive based on its current status. Consequently, AFARM provides an anxiety-free navigation model that allows for a reliable and environmentally friendly driving experience, promoting this alternative mode of transportation.

show abstract

Cybersecurity Risk Analysis of Electric Vehicles Charging Stations

Cited by 45 publications

References 27 publications

Next–Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Models

Next–Generation Intrusion Detection for IoT EVCS: Integrating CNN, LSTM, and GRU Models

Enhancing Microgrid Operation Through Electric Vehicle Integration: A Survey

AFARM: Anxiety-Free Autonomous Routing Model for Electric Vehicles with Dynamic Route Preferences

Contact Info

Product

Resources

About