Robustness Analysis of AI Models in Critical Energy Systems
- URL: http://arxiv.org/abs/2406.14361v1
- Date: Thu, 20 Jun 2024 14:34:36 GMT
- Title: Robustness Analysis of AI Models in Critical Energy Systems
- Authors: Pantelis Dogoulis, Matthieu Jimenez, Salah Ghamizi, Maxime Cordy, Yves Le Traon,
- Abstract summary: This paper analyzes the robustness of state-of-the-art AI-based models for power grid operations under the $N-1$ security criterion.
Our results highlight a significant loss in accuracy following the disconnection of a line.%under this security criterion.
- Score: 17.13189303615842
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper analyzes the robustness of state-of-the-art AI-based models for power grid operations under the $N-1$ security criterion. While these models perform well in regular grid settings, our results highlight a significant loss in accuracy following the disconnection of a line.%under this security criterion. Using graph theory-based analysis, we demonstrate the impact of node connectivity on this loss. Our findings emphasize the need for practical scenario considerations in developing AI methodologies for critical infrastructure.
Related papers
- SafePowerGraph: Safety-aware Evaluation of Graph Neural Networks for Transmission Power Grids [55.35059657148395]
We present SafePowerGraph, the first simulator-agnostic, safety-oriented framework and benchmark for Graph Neural Networks (GNNs) in power systems (PS) operations.
SafePowerGraph integrates multiple PF and OPF simulators and assesses GNN performance under diverse scenarios, including energy price variations and power line outages.
arXiv Detail & Related papers (2024-07-17T09:01:38Z) - Explainable AI for Enhancing Efficiency of DL-based Channel Estimation [1.0136215038345013]
Support of artificial intelligence based decision-making is a key element in future 6G networks.
In such applications, using AI as black-box models is risky and challenging.
We propose a novel-based XAI-CHEST framework that is oriented toward channel estimation in wireless communications.
arXiv Detail & Related papers (2024-07-09T16:24:21Z) - On provable privacy vulnerabilities of graph representations [34.45433384694758]
Graph representation learning (GRL) is critical for extracting insights from complex network structures.
It also raises security concerns due to potential privacy vulnerabilities in these representations.
This paper investigates the structural vulnerabilities in graph neural models where sensitive topological information can be inferred through edge reconstruction attacks.
arXiv Detail & Related papers (2024-02-06T14:26:22Z) - Deep autoregressive density nets vs neural ensembles for model-based
offline reinforcement learning [2.9158689853305693]
We consider a model-based reinforcement learning algorithm that infers the system dynamics from the available data and performs policy optimization on imaginary model rollouts.
This approach is vulnerable to exploiting model errors which can lead to catastrophic failures on the real system.
We show that better performance can be obtained with a single well-calibrated autoregressive model on the D4RL benchmark.
arXiv Detail & Related papers (2024-02-05T10:18:15Z) - It Is Time To Steer: A Scalable Framework for Analysis-driven Attack Graph Generation [50.06412862964449]
Attack Graph (AG) represents the best-suited solution to model and analyze multi-step attacks on computer networks.
This paper introduces an analysis-driven framework for AG generation.
It enables real-time attack path analysis before the completion of the AG generation with a quantifiable statistical significance.
arXiv Detail & Related papers (2023-12-27T10:44:58Z) - U-CE: Uncertainty-aware Cross-Entropy for Semantic Segmentation [11.099838952805325]
We present a novel Uncertainty-aware Cross-Entropy loss (U-CE) that incorporates dynamic predictive uncertainties into the training process by pixel-wise weighting of the well-known cross-entropy loss (CE)
We demonstrate the superiority of U-CE over regular CE training on two benchmark datasets, Cityscapes and ACDC, using two common backbone architectures, ResNet-18 and ResNet-101.
arXiv Detail & Related papers (2023-07-19T12:41:54Z) - On the Robustness of Aspect-based Sentiment Analysis: Rethinking Model,
Data, and Training [109.9218185711916]
Aspect-based sentiment analysis (ABSA) aims at automatically inferring the specific sentiment polarities toward certain aspects of products or services behind social media texts or reviews.
We propose to enhance the ABSA robustness by systematically rethinking the bottlenecks from all possible angles, including model, data, and training.
arXiv Detail & Related papers (2023-04-19T11:07:43Z) - Building Robust Ensembles via Margin Boosting [98.56381714748096]
In adversarial robustness, a single model does not usually have enough power to defend against all possible adversarial attacks.
We develop an algorithm for learning an ensemble with maximum margin.
We show that our algorithm not only outperforms existing ensembling techniques, but also large models trained in an end-to-end fashion.
arXiv Detail & Related papers (2022-06-07T14:55:58Z) - Improving robustness of jet tagging algorithms with adversarial training [56.79800815519762]
We investigate the vulnerability of flavor tagging algorithms via application of adversarial attacks.
We present an adversarial training strategy that mitigates the impact of such simulated attacks.
arXiv Detail & Related papers (2022-03-25T19:57:19Z) - Adaptive network reliability analysis: Methodology and applications to
power grid [0.0]
This study presents the first adaptive surrogate-based Network Reliability Analysis using Bayesian Additive Regression Trees (ANR-BART)
Results indicate that ANR-BART is robust and yields accurate estimates of network failure probability, while significantly reducing the computational cost of reliability analysis.
arXiv Detail & Related papers (2021-09-11T19:58:08Z) - Graph Backdoor [53.70971502299977]
We present GTA, the first backdoor attack on graph neural networks (GNNs)
GTA departs in significant ways: it defines triggers as specific subgraphs, including both topological structures and descriptive features.
It can be instantiated for both transductive (e.g., node classification) and inductive (e.g., graph classification) tasks.
arXiv Detail & Related papers (2020-06-21T19:45:30Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.