arXiv subjects

Mariam Elnour

Publications and source records attributed to Mariam Elnour.

3 recordsLinked to original sources

arXiv2026

Joint Sensor Deployment and Physics-Informed Graph Transformer for Smart Grid Attack Detection

This paper proposes a joint multi-objective optimization framework for strategic sensor placement in power systems to enhance attack detection. A novel physics-informed graph transformer network (PIGTN)-based detection model is proposed. Non-dominated sorting genetic algorithm-II (NSGA-II) jointly optimizes sensor locations and the PIGTN's detection performance, while considering practical constraints. The combinatorial space of feasible sensor placements is explored using NSGA-II, while concurrently training the proposed detector in a closed-loop setting. Compared to baseline sensor placement methods, the proposed framework consistently demonstrates robustness under sensor failures and improvements in detection performance in seven benchmark cases, including the 14, 30, IEEE-30, 39, 57, 118 and the 200 bus systems. By incorporating AC power flow constraints, the proposed PIGTN-based detection model generalizes well to unseen attacks and outperforms other graph network-based variants (topology-aware models), achieving improvements up to 37% in accuracy and 73% in detection rate, with a mean false alarms rate of 0.3%. In addition, optimized sensor layouts significantly improve the performance of power system state estimation, achieving a 61%--98% reduction in the average state error.

cs.NE↗

arXiv2024

On Graph Theory vs. Time-Domain Discrete Event Simulation for Topology-Informed Assessment of Power Grid Cyber Risk

The shift toward more renewable energy sources and distributed generation in smart grids has underscored the significance of modeling and analyzing modern power systems as cyber-physical systems (CPS). This transformation has highlighted the importance of cyber and cyber-physical properties of modern power systems for their reliable operation. Graph theory emerges as a pivotal tool for understanding the complex interactions within these systems, providing a framework for representation and analysis. The challenge is vetting these graph theoretic methods and other estimates of system behavior from mathematical models against reality. High-fidelity emulation and/or simulation can help answer this question, but the comparisons have been understudied. This paper employs graph-theoretic metrics to assess node risk and criticality in three distinct case studies, using a Python-based discrete-event simulation called SimPy. Results for each case study show that combining graph theory and simulation provides a topology-informed security assessment. These tools allow us to identify critical network nodes and evaluate their performance and reliability under a cyber threat such as denial of service threats.

math.NA↗

arXiv2024

Unveiling Hidden Energy Anomalies: Harnessing Deep Learning to Optimize Energy Management in Sports Facilities

Anomaly detection in sport facilities has gained significant attention due to its potential to promote energy saving and optimizing operational efficiency. In this research article, we investigate the role of machine learning, particularly deep learning, in anomaly detection for sport facilities. We explore the challenges and perspectives of utilizing deep learning methods for this task, aiming to address the drawbacks and limitations of conventional approaches. Our proposed approach involves feature extraction from the data collected in sport facilities. We present a problem formulation using Deep Feedforward Neural Networks (DFNN) and introduce threshold estimation techniques to identify anomalies effectively. Furthermore, we propose methods to reduce false alarms, ensuring the reliability and accuracy of anomaly detection. To evaluate the effectiveness of our approach, we conduct experiments on aquatic center dataset at Qatar University. The results demonstrate the superiority of our deep learning-based method over conventional techniques, highlighting its potential in real-world applications. Typically, 94.33% accuracy and 92.92% F1-score have been achieved using the proposed scheme.

cs.CY↗