False Data Injection Attack Detection in a Power Grid Using RNN

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.