The increasing availability of Electric Vehicles (EVs) is driving a shift away from traditional gasoline-powered vehicles. Subsequently, the demand for Electric Vehicle Charging Systems (EVCS) is rising, leading to the significant growth of EVCS as public and private charging infrastructure. The cybersecurity-related risks in EVCS have significantly increased due to the growing network of EVCS. In this context, this paper presents a cybersecurity risk analysis of the network of EVCS. Firstly, the recent advancements in the EVCS network, recent EV adaptation trends, and charging use cases are described as a background of the research area. Secondly, cybersecurity aspects in EVCS have been presented considering infrastructure and protocol-centric vulnerabilities with possible cyber-attack scenarios. Thirdly, threats in EVCS have been validated with real-time data-centric analysis of EV charging sessions. The paper also highlights potential open research issues in EV cyber research as new knowledge for domain researchers and practitioners.

The widespread adoption of Electric Vehicles (EV) has emphasized the urgent need for efficient and secure charging infrastructure. While existing research in EV charging infrastructure has primarily concentrated on minimizing charging time at charging stations(CSs), neglecting security-centric charging optimization, particularly with scaled charging infrastructure considering multiple CSs. To address this gap, this paper presents an enhanced Hybrid-Electric Vehicle Charging Management and Security (H-EVCMS) framework. The H-EVCMS framework is meticulously designed to optimize charging price, manage load balancing, and provide security across multiple CS by leveraging the Open Charge Point Protocol (OCPP). The proposed framework's performance is evaluated by examining various charging scenarios and analyzing the booking and power consumption patterns of each CS. The results demonstrate the advantages of the hybrid approach used by the proposed H-EVCMS over traditional charging infrastructure management, showcasing its potential to address the challenges of scaling EV charging infrastructure while ensuring security and efficiency.

The EV charging network has witnessed significant growth in the UK in the last few years due to the net zero emission target of the government by 2030. The related literature in EV charging management mainly focuses on road-traffic-parameter-based optimization and lacks detail in terms of charging statistics and cyber–security-enabled charging management frameworks. In this context, this paper proposes a novel EV Charging Management and Security (EVCMS) framework using real-time charging statistics and an Open Charge Point Protocol (OCPP). Specifically, a system model for EVCMS is presented considering charging data management and security protocols. An EVCMS framework design is detailed, focusing on charging pricing, optimization, and charging security. The experimental implementation is described in terms of client–server and charge-box-based simulation. The performance of the proposed EVCMS framework is evaluated by considering different charging scenarios and a range of charging-related metrics. An analysis of results and comparative study attest to the benefits of the proposed EVCMS framework for enabling the EV charging ecosystem.

With the rapid adoption of digital payments in India, credit card companies are focusing on customer loyalty and planning rewards to incentivize spending, especially during peak periods like festivals. However, there is a gap in developing a tailored system that optimizes sales and reward structures for these companies. The proposed work addresses this gap by leveraging machine learning techniques to analyze and assess credit card spending patterns and propose design targeted reward programs. Besides this, this study focuses on categories as luxury, travel, groceries, EMIs payments, and others and employs ML methods, using K-Means clustering to segment users based on card types (Silver, Gold, Platinum, and Signature). Feature engineering is another key in improving the model’s understanding and providing insights, particularly in calculating reward points based on various attributes and spending behavior. The usage of original and synthetic datasets ensured scalability and adaptability across different financial domains as well. The results highlight the potential and need of ML to optimize reward allocation and provide real-time predictions, enabling financial institutions to tailor their offerings for increased customer engagement and retention. By aligning rewards with high-margin spending categories and leveraging adaptive frameworks, this study offers strategies to enhance credit card reward programs. The proposed ML model achieved an R² value of 0.99, demonstrating superior accuracy in optimizing reward point distribution.