@inproceedings{e3e75bba5a954290b314cfc6b014810a,
title = "False data injection attack detection in a power grid using RNN",
abstract = "Cyber attacks on Cyber Physical Systems (CPSs), especially on those critical infrastructures poses severe threat on the public security. Among them, a special kind of attack, False Data Injection (FDI), can bypass the surveillance of state-estimation-based bad data detection mechanism silently. In this paper, we exploited the strong ability of Recurrent Neural Network (RNN) on time-series prediction to recognize the potential compromised measurements. It makes our proposed method practicable in real-world scenario that no labeled data is required during all stages of algorithm. An experiment on IEEE-14 bus test system is conducted and shows a promising result that our proposed method is able to detect FDI attack with high precision and high recall.",
keywords = "Cyber security, Detection, False data injection attack, Recurrent neural network",
author = "Qingyu Deng and Jian Sun",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 44th Annual Conference of the IEEE Industrial Electronics Society, IECON 2018 ; Conference date: 20-10-2018 Through 23-10-2018",
year = "2018",
month = dec,
day = "26",
doi = "10.1109/IECON.2018.8591079",
language = "English",
series = "Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5983--5988",
booktitle = "Proceedings",
address = "United States",
}