Summary of A Multi-module Robust Method For Transient Stability Assessment Against False Label Injection Cyberattacks, by Hanxuan Wang et al.
A Multi-module Robust Method for Transient Stability Assessment against False Label Injection Cyberattacks
by Hanxuan Wang, Na Lu, Yinhong Liu, Zhuqing Wang, Zixuan Wang
First submitted to arxiv on: 10 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Multi-Module Robust TSA method (MMR) tackles false label injection (FLI) cyberattacks in transient stability assessment (TSA) datasets by introducing an unsupervised clustering module and a training label corrector. This allows for accurate clustering assignments, rectifying the injected false labels, and enhancing robustness against FLI. The MMR-HIL strategy further improves accuracy and convergence speed through human-in-the-loop re-labeling of potential false samples. Extensive experiments demonstrate the effectiveness of both methods in resisting FLI attacks and correcting contaminated labels. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes new ways to protect deep learning models from cyberattacks that alter their training data. It presents two solutions: MMR, which fixes mistakes in the training data without human help, and MMR-HIL, which involves a person re-labeling some of the data to improve accuracy. The methods are tested and show they can successfully resist these attacks and correct any mistakes. |
Keywords
» Artificial intelligence » Clustering » Deep learning » Unsupervised
