16 papers
Explainable Artificial Intelligence for Industrial Cybersecurity: A Review of Methods, Operational Integration, and Research Challenges
Amr S. Mohamed, Charlotte Fritz, Ahmad Mohammad Saber +5
The increasing digitalization of industrial infrastructure and the convergence of information technology (IT) and operational technology (OT) have expanded the cyberattack surface…
Large Language Models as Explainable Cyberattack Detectors for Energy Industrial Control Systems
Weiyi Kong, Ahmad Mohammad Saber, Amr Youssef +1
In modern energy systems, industrial control systems (ICS) and power-system SCADA require intrusion detection that is not only accurate but also auditable by operators. The ICS int…
An AI-Based Supervisory Measurement Integrity Validation Layer for Cyber-Resilient AC/DC Protection in Inverter-Based Microgrids
Ahmad Mohammad Saber, Ahmed Saber Refae, Davor Svetinovic +4
Line current differential relays (LCDRs) are measurement-driven relays that rely on time-synchronized multi-phase current waveforms to infer internal faults in AC and DC power netw…
Evaluating Jailbreaking Vulnerabilities in LLMs Deployed as Assistants for Smart Grid Operations: A Benchmark Against NERC Standards
Taha Hammadia, Lucas Rea, Ahmad Mohammad Saber +2
The deployment of Large Language Models (LLMs) as assistants in electric grid operations promises to streamline compliance and decision-making but exposes new vulnerabilities to pr…
Dual-Stage LLM Framework for Scenario-Centric Semantic Interpretation in Driving Assistance
Jean Douglas Carvalho, Hugo Taciro Kenji, Ahmad Mohammad Saber +3
Advanced Driver Assistance Systems (ADAS) increasingly rely on learning-based perception, yet safety-relevant failures often arise without component malfunction, driven instead by…
Multimodal Large Language Model Framework for Safe and Interpretable Grid-Integrated EVs
Jean Douglas Carvalho, Hugo Kenji, Ahmad Mohammad Saber +3
The integration of electric vehicles (EVs) into smart grids presents unique opportunities to enhance both transportation systems and energy networks. However, ensuring safe and int…
Glass-Box Deep Learning for FDIA Detection in Nonlinear Automatic Generation Control: A Kolmogorov-Arnold Network Approach
Jehad Jilan, Niranjana Naveen Nambiar, Ahmad Mohammad Saber +3
Automatic Generation Control (AGC) plays a critical role in maintaining power balance across multi-area power systems. However, its complete reliance on remotely communicated measu…
Potential of Quantum Computing Applications for Smart Grid Digital Twins and Future Directions
Arianne Ornella Lemo, Ahmad Mohammad Saber, Deepa Kundur +1
The convergence of digital twin technology and quantum computing is opening new horizons for the modeling, control, and optimization of smart grid systems. This paper reviews the c…
Large Language Model-Based Framework for Explainable Cyberattack Detection in Automatic Generation Control Systems
Muhammad Sharshar, Ahmad Mohammad Saber, Davor Svetinovic +3
The increasing digitization of smart grids has improved operational efficiency but also introduced new cybersecurity vulnerabilities, such as False Data Injection Attacks (FDIAs) t…
Optimal Planning for Enhancing the Resilience of Modern Distribution Systems Against Cyberattacks
Armita Khashayardoost, Ahmad Mohammad Saber, Deepa Kundur
The increasing integration of IoT-connected devices in smart grids has introduced new vulnerabilities at the distribution level. Of particular concern is the potential for cyberatt…
Adversarial Attacks on Deep Learning-Based False Data Injection Detection in Differential Relays
Ahmad Mohammad Saber, Aditi Maheshwari, Amr Youssef +1
The application of Deep Learning-based Schemes (DLSs) for detecting False Data Injection Attacks (FDIAs) in smart grids has attracted significant attention. This paper demonstrates…
Machine Learning-Based Cyberattack Detection and Identification for Automatic Generation Control Systems Considering Nonlinearities
Nour M. Shabar, Ahmad Mohammad Saber, Deepa Kundur
Automatic generation control (AGC) systems play a crucial role in maintaining system frequency across power grids. However, AGC systems' reliance on communicated measurements expos…
A Kolmogorov-Arnold Network for Explainable Detection of Cyberattacks on EV Chargers
Ahmad Mohammad Saber, Max Mauro Dias Santos, Mohammad Al Janaideh +2
The increasing adoption of Electric Vehicles (EVs) and the expansion of charging infrastructure and their reliance on communication expose Electric Vehicle Supply Equipment (EVSE)…
A Novel Approach to Classify Power Quality Signals Using Vision Transformers
Ahmad Mohammad Saber, Alaa Selim, Mohamed M. Hammad +3
With the rapid integration of electronically interfaced renewable energy resources and loads into smart grids, there is increasing interest in power quality disturbances (PQD) clas…
Enhancing Power Quality Event Classification with AI Transformer Models
Ahmad Mohammad Saber, Amr Youssef, Davor Svetinovic +3
Recently, there has been a growing interest in utilizing machine learning for accurate classification of power quality events (PQEs). However, most of these studies are performed a…
Learning-Based Detection of Malicious Volt-VAr Control Parameters in Smart Inverters
Ahmad Mohammad Saber, Amr Youssef, Davor Svetinovic +2
Distributed Volt-Var Control (VVC) is a widely used control mode of smart inverters. However, necessary VVC curve parameters are remotely communicated to the smart inverter, which…