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Found 5 papers, 1 papers with code
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.
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.
Hybrid Symbolic-Numeric Library for Power System Modeling and Analysis
This paper proposes a two-layer hybrid library consisted of a symbolic layer for descriptive modeling and a numeric layer for vector-based numerical computation.
