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Found 8 papers, 6 papers with code
E2E-AT: A Unified Framework for Tackling Uncertainty in Task-aware End-to-end Learning
Successful machine learning involves a complete pipeline of data, model, and downstream applications.
Task-Aware Machine Unlearning and Its Application in Load Forecasting
To balance between unlearning completeness and model performance, a performance-aware algorithm is proposed by evaluating the sensitivity of local model parameter change using influence function and sample re-weighting.
Availability Adversarial Attack and Countermeasures for Deep Learning-based Load Forecasting
To tackle this attack, an adversarial training algorithm is proposed.
Blending Data and Physics Against False Data Injection Attack: An Event-Triggered Moving Target Defence Approach
However, the uncontrollable false positive rate of the data-driven detector and the extra cost of frequent MTD usage limit their wide applications.
Cyber-Physical Risk Assessment for False Data Injection Attacks Considering Moving Target Defences
In this paper, we examine the factors that influence the success of false data injection (FDI) attacks in the context of both cyber and physical styles of reinforcement.
Robust Moving Target Defence Against False Data Injection Attacks in Power Grids
Recently, moving target defence (MTD) has been proposed to thwart false data injection (FDI) attacks in power system state estimation by proactively triggering the distributed flexible AC transmission system (D-FACTS) devices.
Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks
This paper presents a problem in power networks that creates an exciting and yet challenging real-world scenario for application of multi-agent reinforcement learning (MARL).
Multi-agent Reinforcement Learning
reinforcement-learning
+1
A Deep Learning based Detection Method for Combined Integrity-Availability Cyber Attacks in Power System
As one of the largest and most complex systems on earth, power grid (PG) operation and control have stepped forward as a compound analysis on both physical and cyber layers which makes it vulnerable to assaults from economic and security considerations.
