Summary of False Data Injection Attack Detection in Edge-based Smart Metering Networks with Federated Learning, by Md Raihan Uddin et al.
False Data Injection Attack Detection in Edge-based Smart Metering Networks with Federated Learning
by Md Raihan Uddin, Ratun Rahman, Dinh C. Nguyen
First submitted to arxiv on: 2 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR)
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 research develops a privacy-preserved false data injection (FDI) attack detection framework for smart meter networks using federated learning (FL) and edge computing. The framework allows distributed edge servers to run an ML-based FDI attack detection model, sharing the trained model with the grid operator without sharing raw data. This approach addresses privacy concerns by not disclosing household energy usage patterns. Simulation results demonstrate the efficiency of the proposed FL method compared to a conventional method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to detect false data injection attacks in smart meter networks. It uses a special kind of machine learning that doesn’t require sharing personal information about how much energy people use. Instead, it lets different locations on the network work together to develop a strong detection model. This approach keeps personal information private while still being able to detect when someone is trying to hack into the system. |
Keywords
* Artificial intelligence * Federated learning * Machine learning
