Summary of Unleashing the Power Of Unlabeled Data: a Self-supervised Learning Framework For Cyber Attack Detection in Smart Grids, by Hanyu Zeng et al.
Unleashing the Power of Unlabeled Data: A Self-supervised Learning Framework for Cyber Attack Detection in Smart Grids
by Hanyu Zeng, Pengfei Zhou, Xin Lou, Zhen Wei Ng, David K.Y. Yau, Marianne Winslett
First submitted to arxiv on: 22 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 self-supervised learning-based framework for detecting and identifying various types of cyber attacks in smart grids leverages the BERT model from natural language processing, utilizing massive unlabeled sensing data. The framework learns generalizable representations that capture distinctive patterns of different attacks, and can train a task-specific classifier using very small amounts of labeled data. To address data imbalance issues common in real-world training datasets, a new loss function called separate mean error (SME) is introduced. Experimental results on a 5-area power grid system with 37 buses demonstrate the superior performance of this framework compared to existing approaches, even when only a limited portion of labeled data are available. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper proposes a way to detect cyber attacks in smart grids using artificial intelligence. Currently, many smart grids rely on information and communication technologies (ICTs), but these can also make them more vulnerable to cyber attacks. The new framework uses a special kind of AI model called BERT to learn from unlabeled data and identify different types of attacks. It works even when there is very little labeled data available. The results show that this approach is better than existing methods, especially in situations where there is limited information about the attacks. |
Keywords
» Artificial intelligence » Bert » Loss function » Natural language processing » Self supervised
