Cybersecurity in Vehicle-to-Grid (V2G) Systems: A Systematic Review

• URL: http://arxiv.org/abs/2503.15730v1
• Date: Wed, 19 Mar 2025 22:55:40 GMT
• Title: Cybersecurity in Vehicle-to-Grid (V2G) Systems: A Systematic Review
• Abstract summary: We identified and reviewed 133 V2G cybersecurity studies.<n>Most studies focused on protecting V2G systems against cyber threats.<n>Only seven studies addressed the recovery aspect of the CRML function.<n>None have focused on the linkage between the behaviour of EV users and the cybersecurity of V2G systems.
• Score: 1.1827829754757404
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a systematic review of recent advancements in V2G cybersecurity, employing the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework for detailed searches across three journal databases and included only peer-reviewed studies published between 2020 and 2024 (June). We identified and reviewed 133 V2G cybersecurity studies and found five important insights on existing V2G cybersecurity research. First, most studies (103 of 133) focused on protecting V2G systems against cyber threats, while only seven studies addressed the recovery aspect of the CRML (Cybersecurity Risk Management Lifecycle) function. Second, existing studies have adequately addressed the security of EVs and EVCS (EV charging stations) in V2G systems (112 and 81 of 133 studies, respectively). However, none have focused on the linkage between the behaviour of EV users and the cybersecurity of V2G systems. Third, physical access, control-related vulnerabilities, and user behaviour-related attacks in V2G systems are not addressed significantly. Furthermore, existing studies overlook vulnerabilities and attacks specific to AI and blockchain technologies. Fourth, blockchain, artificial intelligence (AI), encryption, control theory, and optimisation are the main technologies used, and finally, the inclusion of quantum safety within encryption and AI models and AI assurance (AIA) is in a very early stage; only two and one of 133 studies explicitly addressed quantum safety and AIA through explainability. By providing a holistic perspective, this study identifies critical research gaps and outlines future directions for developing robust end-to-end cybersecurity solutions to safeguard V2G systems and support global sustainability goals.

Related papers

• AI-driven Intrusion Detection for UAV in Smart Urban Ecosystems: A Comprehensive Survey [3.26654570054705]
  This paper provides a structured, evidence-based synthesis of UAV applications in smart cities and their associated security challenges.<n>We categorize these challenges into two primary classes: 1) cyber-attacks targeting the communication infrastructure of UAVs and 2) unwanted or unauthorized physical intrusions by UAVs themselves.<n>We analyze how AI-based methods, such as machine/deep learning for anomaly detection and computer vision for object recognition, can play a pivotal role in enhancing UAV security.
  arXiv  Detail & Related papers  (2026-01-27T08:26:05Z)
• Cyberscurity Threats and Defense Mechanisms in IoT network [6.149504226373647]
  The rapid proliferation of Internet of Things technologies, projected to exceed 30 billion interconnected devices by 2030, has significantly escalated the complexity of cybersecurity challenges.<n>This survey aims to provide a comprehensive analysis of vulnerabilities, threats, and defense mechanisms, focusing on the integration of network and application layers within real-time monitoring and decision-making systems.
  arXiv  Detail & Related papers  (2026-01-02T04:06:03Z)
• An Empirical Study on the Security Vulnerabilities of GPTs [48.12756684275687]
  GPTs are one kind of customized AI agents based on OpenAI's large language models.<n>We present an empirical study on the security vulnerabilities of GPTs.
  arXiv  Detail & Related papers  (2025-11-28T13:30:25Z)
• International AI Safety Report 2025: First Key Update: Capabilities and Risk Implications [118.49965571969089]
  This update examines how AI capabilities have improved since the first AI Safety Report.<n>It focuses on key risk areas where substantial new evidence warrants updated assessments.
  arXiv  Detail & Related papers  (2025-10-15T15:13:49Z)
• Adversarial Defense in Cybersecurity: A Systematic Review of GANs for Threat Detection and Mitigation [8.920024578035386]
  Generative Adversarial Networks (GANs) act as both powerful attack enablers and promising defenses.<n>This survey systematically reviews GAN-based adversarial defenses in cybersecurity.<n>We introduce a four-dimensional taxonomy spanning defensive function, GAN architecture, cybersecurity domain, and adversarial threat model.
  arXiv  Detail & Related papers  (2025-09-24T07:17:57Z)
• Never Compromise to Vulnerabilities: A Comprehensive Survey on AI Governance [211.5823259429128]
  We propose a comprehensive framework integrating technical and societal dimensions, structured around three interconnected pillars: Intrinsic Security, Derivative Security, and Social Ethics.<n>We identify three core challenges: (1) the generalization gap, where defenses fail against evolving threats; (2) inadequate evaluation protocols that overlook real-world risks; and (3) fragmented regulations leading to inconsistent oversight.<n>Our framework offers actionable guidance for researchers, engineers, and policymakers to develop AI systems that are not only robust and secure but also ethically aligned and publicly trustworthy.
  arXiv  Detail & Related papers  (2025-08-12T09:42:56Z)
• Generative AI-Empowered Secure Communications in Space-Air-Ground Integrated Networks: A Survey and Tutorial [107.26005706569498]
  Space-air-ground integrated networks (SAGINs) face unprecedented security challenges due to their inherent characteristics.<n>Generative AI (GAI) is a transformative approach that can safeguard SAGIN security by synthesizing data, understanding semantics, and making autonomous decisions.
  arXiv  Detail & Related papers  (2025-08-04T01:42:57Z)
• Report on NSF Workshop on Science of Safe AI [75.96202715567088]
  New advances in machine learning are leading to new opportunities to develop technology-based solutions to societal problems.<n>To fulfill the promise of AI, we must address how to develop AI-based systems that are accurate and performant but also safe and trustworthy.<n>This report is the result of the discussions in the working groups that addressed different aspects of safety at the workshop.
  arXiv  Detail & Related papers  (2025-06-24T18:55:29Z)
• A Comprehensive Survey of Unmanned Aerial Systems' Risks and Mitigation Strategies [3.636441947326793]
  This survey summarizes the cybersecurity vulnerabilities in several phases of UAV deployment.<n>We perform an analysis of both UAS-specific and non-UAS-specific mitigation strategies that are applicable within the UAS domain.<n>We present relevant cybersecurity standards and their recommendations in the UAS context.
  arXiv  Detail & Related papers  (2025-06-12T03:30:19Z)
• LLM-Assisted Proactive Threat Intelligence for Automated Reasoning [2.0427650128177]
  This research presents a novel approach to enhance real-time cybersecurity threat detection and response. We integrate large language models (LLMs) and Retrieval-Augmented Generation (RAG) systems with continuous threat intelligence feeds.
  arXiv  Detail & Related papers  (2025-04-01T05:19:33Z)
• AISafetyLab: A Comprehensive Framework for AI Safety Evaluation and Improvement [73.0700818105842]
  We introduce AISafetyLab, a unified framework and toolkit that integrates representative attack, defense, and evaluation methodologies for AI safety. AISafetyLab features an intuitive interface that enables developers to seamlessly apply various techniques. We conduct empirical studies on Vicuna, analyzing different attack and defense strategies to provide valuable insights into their comparative effectiveness.
  arXiv  Detail & Related papers  (2025-02-24T02:11:52Z)
• Cyber security of OT networks: A tutorial and overview [1.4361933642658902]
  This manuscript explores the cybersecurity challenges of Operational Technology (OT) networks.<n> OT systems increasingly integrate with Information Technology (IT) systems due to Industry 4.0 initiatives.<n>The study examines key components of OT systems, such as SCADA (Supervisory Control and Data Acquisition), PLCs (Programmable Logic Controllers), and RTUs (Remote Terminal Units)
  arXiv  Detail & Related papers  (2025-02-19T17:23:42Z)
• Open Problems in Machine Unlearning for AI Safety [61.43515658834902]
  Machine unlearning -- the ability to selectively forget or suppress specific types of knowledge -- has shown promise for privacy and data removal tasks.<n>In this paper, we identify key limitations that prevent unlearning from serving as a comprehensive solution for AI safety.
  arXiv  Detail & Related papers  (2025-01-09T03:59:10Z)
• Counter Denial of Service for Next-Generation Networks within the Artificial Intelligence and Post-Quantum Era [2.156208381257605]
  DoS attacks are becoming increasingly sophisticated and easily executable. State-of-the-art systematization efforts have limitations such as isolated DoS countermeasures. The emergence of quantum computers is a game changer for DoS from attack and defense perspectives.
  arXiv  Detail & Related papers  (2024-08-08T18:47:31Z)
• Confronting the Reproducibility Crisis: A Case Study of Challenges in Cybersecurity AI [0.0]
  A key area in AI-based cybersecurity focuses on defending deep neural networks against malicious perturbations. We attempt to validate results from prior work on certified robustness using the VeriGauge toolkit. Our findings underscore the urgent need for standardized methodologies, containerization, and comprehensive documentation.
  arXiv  Detail & Related papers  (2024-05-29T04:37:19Z)
• Sok: Comprehensive Security Overview, Challenges, and Future Directions of Voice-Controlled Systems [10.86045604075024]
  The integration of Voice Control Systems into smart devices accentuates the importance of their security. Current research has uncovered numerous vulnerabilities in VCS, presenting significant risks to user privacy and security. This study introduces a hierarchical model structure for VCS, providing a novel lens for categorizing and analyzing existing literature in a systematic manner. We classify attacks based on their technical principles and thoroughly evaluate various attributes, such as their methods, targets, vectors, and behaviors.
  arXiv  Detail & Related papers  (2024-05-27T12:18:46Z)
• Trust-based Approaches Towards Enhancing IoT Security: A Systematic Literature Review [3.0969632359049473]
  This research paper presents a systematic literature review on the Trust-based cybersecurity security approaches for IoT. We highlighted the common trust-based mitigation techniques in existence for dealing with these threats. Several open issues were highlighted, and future research directions presented.
  arXiv  Detail & Related papers  (2023-11-20T12:21:35Z)
• Graph Mining for Cybersecurity: A Survey [61.505995908021525]
  The explosive growth of cyber attacks nowadays, such as malware, spam, and intrusions, caused severe consequences on society. Traditional Machine Learning (ML) based methods are extensively used in detecting cyber threats, but they hardly model the correlations between real-world cyber entities. With the proliferation of graph mining techniques, many researchers investigated these techniques for capturing correlations between cyber entities and achieving high performance.
  arXiv  Detail & Related papers  (2023-04-02T08:43:03Z)
• Proceedings of the Artificial Intelligence for Cyber Security (AICS) Workshop at AAAI 2022 [55.573187938617636]
  The workshop will focus on the application of AI to problems in cyber security. Cyber systems generate large volumes of data, utilizing this effectively is beyond human capabilities.
  arXiv  Detail & Related papers  (2022-02-28T18:27:41Z)
• Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
  Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance. We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems. We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
  arXiv  Detail & Related papers  (2020-10-19T13:09:31Z)
• Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain [58.30296637276011]
  This paper summarizes the latest research on adversarial attacks against security solutions based on machine learning techniques. It is the first to discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain.
  arXiv  Detail & Related papers  (2020-07-05T18:22:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.