Addressing Weak Authentication like RFID, NFC in EVs and EVCs using AI-powered Adaptive Authentication

• URL: http://arxiv.org/abs/2508.19465v1
• Date: Tue, 26 Aug 2025 22:54:24 GMT
• Title: Addressing Weak Authentication like RFID, NFC in EVs and EVCs using AI-powered Adaptive Authentication
• Authors: Onyinye Okoye,
• Abstract summary: The rapid expansion of the Electric Vehicles (EVs) and Electric Vehicle Charging Systems (EVCs) has introduced new cybersecurity challenges.<n>Traditional authentication mechanisms like Radio Frequency Identification (RFID) and Near Field Communication (NFC) rely on static identifiers and weak encryption.<n>This study explores an AI-powered adaptive authentication framework designed to overcome these shortcomings by integrating machine learning, anomaly detection, behavioral analytics, and contextual risk assessment.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid expansion of the Electric Vehicles (EVs) and Electric Vehicle Charging Systems (EVCs) has introduced new cybersecurity challenges, specifically in authentication protocols that protect vehicles, users, and energy infrastructure. Although widely adopted for convenience, traditional authentication mechanisms like Radio Frequency Identification (RFID) and Near Field Communication (NFC) rely on static identifiers and weak encryption, making them highly vulnerable to attack vectors such as cloning, relay attacks, and signal interception. This study explores an AI-powered adaptive authentication framework designed to overcome these shortcomings by integrating machine learning, anomaly detection, behavioral analytics, and contextual risk assessment. Grounded in the principles of Zero Trust Architecture, the proposed framework emphasizes continuous verification, least privilege access, and secure communication. Through a comprehensive literature review, this research evaluates current vulnerabilities and highlights AI-driven solutions to provide a scalable, resilient, and proactive defense. Ultimately, the research findings conclude that adopting AI-powered adaptive authentication is a strategic imperative for securing the future of electric mobility and strengthening digital trust across the ecosystem. Keywords: weak authentication, RFID, NFC, ML, AI-powered adaptive authentication, relay attacks, cloning, eavesdropping, MITM attacks, Zero Trust Architecture

Related papers

• Deep Learning-Based Multi-Factor Authentication: A Survey of Biometric and Smart Card Integration Approaches [4.345882429229813]
  Multi-Factor Authentication (MFA) combines knowledge-based factors (passwords, PINs), possession-based factors (smart cards, tokens), and inherence-based factors (biometric traits)<n>Recent breakthroughs in deep learning have transformed the capabilities of biometric systems.<n>Smart card technologies have evolved to include on-chip biometric verification, cryptographic processing, and secure storage.
  arXiv  Detail & Related papers  (2025-10-04T18:34:16Z)
• A Lightweight Authentication and Key Agreement Protocol Design for FANET [19.77683707225124]
  The advancement of low-altitude intelligent networks enables unmanned aerial vehicle (UAV) interconnection via flying ad-hoc networks (FANETs)<n>Existing multi-factor and public-key cryptography protocols are vulnerable due to their reliance on stored sensitive information.<n>This paper proposes a lightweight authentication and key agreement protocol for FANETs, integrating physical unclonable functions with dynamic credential management and lightweight cryptographic primitives.
  arXiv  Detail & Related papers  (2025-09-22T07:03:23Z)
• 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)
• Fuse and Federate: Enhancing EV Charging Station Security with Multimodal Fusion and Federated Learning [3.783757921469148]
  Rapid global adoption of electric vehicles has established electric vehicle supply equipment (EVSE) as a critical component of smart grid infrastructure.<n>EVSE systems face significant cybersecurity challenges, including network reconnaissance, backdoor intrusions, and distributed denial-of-service (DDoS) attacks.<n>This paper proposes a novel intrusion detection framework that leverages multimodal data sources, including network traffic and kernel events, to identify complex attack patterns.
  arXiv  Detail & Related papers  (2025-06-07T09:34:10Z)
• Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things [61.43014629640404]
  Zero-Trust Foundation Models (ZTFMs) embed zero-trust security principles into the lifecycle of foundation models (FMs) for Internet of Things (IoT) systems.<n>ZTFMs can enable secure, privacy-preserving AI across distributed, heterogeneous, and potentially adversarial IoT environments.
  arXiv  Detail & Related papers  (2025-05-26T06:44:31Z)
• AI-Powered Anomaly Detection with Blockchain for Real-Time Security and Reliability in Autonomous Vehicles [1.1797787239802762]
  We develop a new framework that combines the power of Artificial Intelligence (AI) for real-time anomaly detection with blockchain technology to detect and prevent any malicious activity.<n>This framework employs a decentralized platform for securely storing sensor data and anomaly alerts in a blockchain ledger for data incorruptibility and authenticity.<n>This makes the AV system more resilient to attacks from both cyberspace and hardware component failure.
  arXiv  Detail & Related papers  (2025-05-10T12:53:28Z)
• ACRIC: Securing Legacy Communication Networks via Authenticated Cyclic Redundancy Integrity Check [98.34702864029796]
  Recent security incidents in safety-critical industries exposed how the lack of proper message authentication enables attackers to inject malicious commands or alter system behavior.<n>These shortcomings have prompted new regulations that emphasize the pressing need to strengthen cybersecurity.<n>We introduce ACRIC, a message authentication solution to secure legacy industrial communications.
  arXiv  Detail & Related papers  (2024-11-21T18:26:05Z)
• A Survey and Comparative Analysis of Security Properties of CAN Authentication Protocols [92.81385447582882]
  The Controller Area Network (CAN) bus leaves in-vehicle communications inherently non-secure. This paper reviews and compares the 15 most prominent authentication protocols for the CAN bus. We evaluate protocols based on essential operational criteria that contribute to ease of implementation.
  arXiv  Detail & Related papers  (2024-01-19T14:52:04Z)
• DynamiQS: Quantum Secure Authentication for Dynamic Charging of Electric Vehicles [61.394095512765304]
  Dynamic Wireless Power Transfer (DWPT) is a novel technology that allows charging an electric vehicle while driving. Recent advancements in quantum computing jeopardize classical public key cryptography. We propose DynamiQS, the first post-quantum secure authentication protocol for dynamic wireless charging.
  arXiv  Detail & Related papers  (2023-12-20T09:40:45Z)
• Blockchain-based Zero Trust on the Edge [5.323279718522213]
  This paper proposes a novel approach based on Zero Trust Architecture (ZTA) extended with blockchain to further enhance security. The blockchain component serves as an immutable database for storing users' requests and is used to verify trustworthiness by analyzing and identifying potentially malicious user activities. We discuss the framework, processes of the approach, and the experiments carried out on a testbed to validate its feasibility and applicability in the smart city context.
  arXiv  Detail & Related papers  (2023-11-28T12:43:21Z)
• When Authentication Is Not Enough: On the Security of Behavioral-Based Driver Authentication Systems [53.2306792009435]
  We develop two lightweight driver authentication systems based on Random Forest and Recurrent Neural Network architectures. We are the first to propose attacks against these systems by developing two novel evasion attacks, SMARTCAN and GANCAN. Through our contributions, we aid practitioners in safely adopting these systems, help reduce car thefts, and enhance driver security.
  arXiv  Detail & Related papers  (2023-06-09T14:33:26Z)

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.