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

TL;DR
This paper proposes an AI-powered adaptive authentication framework to enhance security in EVs and EVCs by addressing vulnerabilities in traditional RFID and NFC systems through continuous, context-aware verification methods.
Contribution
It introduces a novel AI-driven adaptive authentication approach based on machine learning and behavioral analytics, improving security over static methods in electric vehicle ecosystems.
Findings
AI-powered adaptive authentication effectively detects and prevents relay and cloning attacks.
The framework enhances security by implementing continuous verification and contextual risk assessment.
Adoption of the approach increases resilience against common cyber threats in EV infrastructure.
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…
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