Guest Editor(s)
Dr. Tehseen Mazhar
Email: tehseenmazhar719@gmail.com
Affiliation: School of Computer Science, National College of Business Administration and Economics, Lahore, Pakistan
Homepage:
Research Interests: energy security, electric vehicle, smart grid, IoT, blockchain, machine learning, cloud computing
Dr. Naila Sammar NaZ
Email: nailanaz@ncbae.edu.pk
Affiliation: School of Computer Science, National College of Business Administration and Economics, Lahore, Pakistan
Homepage:
Research Interests: energy security, artificila intelligence, smart grid, IoT, blockchain, machine learning
Dr. Fahad Ahmed
Email: fahadahmed4617@gmail.com
Affiliation: School of Computer Science, National College of Business Administration and Economics, Lahore, Pakistan
Homepage:
Research Interests: energy security, energy system, healthcare, explainable artificial intelligence, cloud computing, blockchain, machine learning, deep learning
Summary
This special issue discusses the growing cybersecurity threats of the energy systems in the modern world and the spread of IoT devices while introducing advanced AI- and machine learning-based solutions to the problem.
The primary focus will be on improved security, resilience, and energy efficiency of smart grids, and the seamless integration of electric vehicles (EVs) by leveraging IoT for effective charging and load management, and for threat detection.
It will give the researchers a chance to contribute new AI/ML models, algorithms, Review papers, SLR and case studies that will provide the foundation for secure and sustainable energy infrastructure of the future.
In particular, Energy Security lies at the heart of this Special Issue. As cyberattacks on critical energy infrastructure become more frequent and sophisticated, ensuring the reliable, uninterrupted supply of electricity and fuel has become a national and global priority. The integration of IoT and AI introduces both vulnerabilities and powerful defensive capabilities. By strengthening the cyber-resilience of smart grids, securing EV charging networks, and protecting distributed energy resources against adversarial threats, we aim to advance the triple goals of security, reliability, and sustainability in modern energy systems.
The topics of interest include:
- AI-Driven Threat Detection and Response for Smart Grid Cybersecurity
- Machine Learning Models for Anomaly Detection in Energy IoT Networks
- Cyber-Resilient Architectures for Distributed Energy Resource Management
- Secure Integration of Electric Vehicles into Smart Charging Infrastructure
- Blockchain-Based Authentication and Data Integrity for Energy Transactions
- Adversarial Attack and Defense Mechanisms in AI-Powered Energy Systems
- Real-Time Intrusion Detection Systems for SCADA and Industrial Control Networks
- Privacy-Preserving Federated Learning for Smart Meter Data Analytics
- Risk Assessment Frameworks for Cyber-Physical Energy Systems
- Digital Twin-Based Security Monitoring for Critical Energy Assets
- Zero-Trust Security Models for IoT-Enabled Power Grids
- Post-Quantum Cryptography for Long-Term Security of Energy Communications
- Explainable AI (XAI) for Trustworthy Cybersecurity Decision-Making in Energy
- Policy and Regulatory Challenges for Securing National Energy Infrastructure
- Case Studies and Lessons Learned from Recent Cyber Incidents in the Energy Sector
Keywords
cybersecurity challenges, AI/ML-based solutions, IoT integration, smart grids security & resilience efficiency & optimization , electric vehicle integration, modern energy systems
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