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AI-Driven Predictive Approaches for Mitigating Cyber Attacks on Electric Vehicle Charging Networks in Public Infrastructure
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Author(s): Paritosh Biswas (Marwadi University, India), Sushil Kumar Singh (Marwadi University, India)and Habib Khan (Gachon University, South Korea)
Copyright: 2026
Pages: 48
Source title:
Transforming Public Administration Through AI-Driven Predictive Analytics
Source Author(s)/Editor(s): Pradeep Kumar (Infrastructure University Kuala Lumpur, Malaysia), Abu Bakar Abdul Hamid (Infrastructure University Kuala Lumpur, Malaysia), Parma Nand (Sharda University, India)and Rajeev Kumar (Moradabad Institute of Technology, India)
DOI: 10.4018/979-8-3373-3760-9.ch001
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Abstract
The rapid adoption of Electric Vehicles (EVs) is transforming sustainable mobility and creating new demands for secure public charging infrastructure. The digital integration of EV charging networks creates vulnerabilities exposed to various cyber threats, including denial-of-service, malware, ransomware, and man-in-the-middle attacks are significant concerns. These vulnerabilities can lead to service disruption, financial loss, and reduced public trust. This chapter provides a comprehensive review of cybersecurity risks within the EV infrastructure, emphasizing the role of AI-driven approaches in mitigating risks through the use of machine learning, anomaly detection, and adaptive defence strategies. Predictive analytics help identify and respond to emerging threats. The chapter also covers secure communication protocols and encryption mechanisms. Integration of predictive intelligence in the development of secure and resilient public EV infrastructure ensures operational continuity and trust in digital EV infrastructure.
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