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DNN	修改了攻击方式，使其在全连接层模型上的效果更显著	2024-01-28 20:30:04 +08:00
Load_forecast_adversaries.py	修改了攻击方式，使其在全连接层模型上的效果更显著	2024-01-28 20:30:04 +08:00
Neural_Net_Module.py	修改了攻击方式，使其在全连接层模型上的效果更显著	2024-01-28 20:30:04 +08:00
README.md	修改了攻击方式，使其在全连接层模型上的效果更显著	2024-01-28 20:30:04 +08:00
Readings.md	修改了攻击方式，使其在全连接层模型上的效果更显著	2024-01-28 20:30:04 +08:00
building_data.csv	修改了攻击方式，使其在全连接层模型上的效果更显著	2024-01-28 20:30:04 +08:00
utils.py	修改了攻击方式，使其在全连接层模型上的效果更显著	2024-01-28 20:30:04 +08:00

README.md

Power_adversary

The code repo for Is Machine Learning in Power Systems Vulnerable?

Paper accepted to SmartGridComm2018, Workshop on AI in Energy Systems.

Authors: Yize Chen, Yushi Tan and Deepjyoti Deka

University of Washington and Los Alamos National Laboratory.

Introduction

We look into the vulnerabilities of ML algorithms in power systems, and craft specific attacks on power systems with different applications.

Usage

To exploit the algorithmic vulnerabilities, we consider the classification and forecasting case in power systems. Directly run the Python files and test the model accuracy before and after the attack.

Contact: yizechen@uw.edu