Neetesh Saxena

The rapid advancement of Electric Vehicles (EVs) has made charging stations a critical component of the overall EV charging infrastructure, but their increasing complexity also extends the attack surface, leaving them vulnerable to exploitation. This article identifies key security issues and challenges facing EV charging infrastructure, especially against mass scale cyber threats, explores gaps within existing solutions, and proposes an architecture and recommendations based on identified gaps. By offering targeted recommendations and insights, we provide a guideline to strengthen EV charging systems and their infrastructure against emerging cyber threats.

Cyber‐Physical Systems (CPS) are central to modern smart infrastructures, enabling intelligent processes that handle large volumes of data while ensuring security, safety, reliability, and resilience. The integration of Artificial Intelligence (AI), particularly deep learning, with CPS and Internet of Things (IoT) technologies is driving advancements in Industry 4.0, smart grids, and intelligent transportation systems. AI enhances cybersecurity by enabling anomaly detection, continuous monitoring, network scanning, log analysis, and classification of data into legitimate and malicious categories, allowing real‐time threat identification and proactive mitigation. At the same time, the adoption of CPS and Industrial Internet of Things (IIoT) introduces significant cybersecurity challenges, as security incidents can directly affect humans and critical assets. This convergence of AI and cybersecurity provides a dual advantage: improving operational efficiency while reinforcing defences against evolving cyber‐physical threats. By enabling real‐time threat detection, resilient response, and robust protection of both infrastructure and AI algorithms, CPS can maintain safety, reliability, and performance under complex cyber and physical stressors. This special issue highlights research at the intersection of AI, cybersecurity, and CPS/IIoT, focusing on innovations that enhance resilience, protect critical systems, and support the sustainable growth of smart industrial and urban ecosystems.

Despite edge computing reducing communication delays associated with cloud computing, privacy concerns remain a significant challenge when sharing data from edge-based consumer electronics (CE) or Internet-of-Things (IoT) devices. Ciphertext policy attribute-based encryption (CP-ABE) is a cryptographic tool that facilitates intricate and refined access control. It can deliver more flexible, secure, compact, and effective access control policies for the cloud data. In the case of a conventional CP-ABE scheme, the keys can only be issued and disseminated by a trusted central authority (CA). However, if the CA is compromised, the entire system becomes more susceptible to assaults or failures, which leads to a single-point failure. In this article, we propose a Ring-Learning with Errors (Ring-LWE)-based MA-CP-ABE scheme that is effectively resolved by the multi-authority CP-ABE (MA-CP-ABE), and it ensures the security against quantum attacks. The threshold secret sharing by Shamir and the Lagrange interpolation formula are applied in the key-generation and decryption procedures of the proposed scheme, which make it easier to segment and restore the private keys. The proposed scheme is implemented in the CE-enabled IoT-based smart healthcare applications using the blockchain technology as a secure storage. A detailed comparative study, security analysis and experimental results with the existing relevant schemes shows that the proposed scheme exhibits superior security and better efficiency as compared to other schemes, demonstrating its feasibility in practical IoT-based healthcare applications.

Smart grid operators use load forecasting algorithms to predict energy load for the reliable and economical operation of the electricity grid. COVID‐19 pandemic‐like situations (PLS) can significantly impact energy load demand due to uncertainties in factors such as regulatory orders, pandemic severity and human behavioural patterns. Additionally, in a smart grid, cyberattacks can manipulate forecasted load data, leading to suboptimal decisions, economic losses and potential blackouts. Forecasting load during these situations is challenging for traditional load forecasting tools, as they struggle to identify cyberattacks amidst uncertain load demand, where cyberattacks may mimic pandemic‐like load patterns. Traditional forecasting methods do not incorporate factors related to pandemics and cyberattacks. Recent studies have focused on forecasting by considering factors such as COVID‐19 cases, social distancing, weather, and temperature but fail to account for the impact of regulatory orders and pandemic severity. They also lack the ability to differentiate between normal and anomalous forecasts and classify the type of attack in anomalous data. This paper presents a tool for short‐term load forecasting, anomaly detection and cyberattack classification for pandemic‐like situations (PLS). The proposed short‐term load forecasting algorithm uses a weighted moving average and an adjustment factor incorporating regulatory orders and pandemic severity, making it computationally efficient and deterministic. Additionally, the proposed anomaly detection and cyberattack classification algorithm provides robust options for detecting anomalies and classifying various types of cyberattacks. The proposed tool has been evaluated using K‐Fold cross‐validation to improve generalisability and reduce overfitting. The mean squared error (MSE) was used to measure prediction accuracy and detect discrepancies. It has been analysed and tested on real‐load data from the State Load Dispatch Centre (SLDC), Delhi, of the Indian National Grid.