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Found 7 papers, 1 papers with code
Effects of Real-Life Traffic Sign Alteration on YOLOv7- an Object Recognition Model
The widespread adoption of Image Processing has propelled Object Recognition (OR) models into essential roles across various applications, demonstrating the power of AI and enabling crucial services.
Graph Neural Network Framework for Security Assessment Informed by Topological Measures
Electrical variables as input variables of the model are mainly considered to indicate the power system operation as secure or insecure, according to the reliability criteria for contingency scenarios.
Exploring Physical-Based Constraints in Short-Term Load Forecasting: A Defense Mechanism Against Cyberattack
It has been shown that the power system state estimation is vulnerable to false data injection attacks.
Towards Adversarial-Resilient Deep Neural Networks for False Data Injection Attack Detection in Power Grids
False data injection attacks (FDIAs) pose a significant security threat to power system state estimation.
Exploiting Vulnerabilities of Deep Learning-based Energy Theft Detection in AMI through Adversarial Attacks
In this work, we study the vulnerabilities of DL-based energy theft detection through adversarial attacks, including single-step attacks and iterative attacks.
SearchFromFree: Adversarial Measurements for Machine Learning-based Energy Theft Detection
Energy theft causes large economic losses to utility companies around the world.
ConAML: Constrained Adversarial Machine Learning for Cyber-Physical Systems
We study the potential vulnerabilities of ML applied in CPSs by proposing Constrained Adversarial Machine Learning (ConAML), which generates adversarial examples that satisfy the intrinsic constraints of the physical systems.
