Visible to the public Semi-Supervised False Data Detection Using Gated Recurrent Units and Threshold Scoring Algorithm

TitleSemi-Supervised False Data Detection Using Gated Recurrent Units and Threshold Scoring Algorithm
Publication TypeConference Paper
Year of Publication2021
AuthorsParizad, Ali, Hatziadoniu, Constantine
Conference Name2021 IEEE Power & Energy Society General Meeting (PESGM)
KeywordsAttack Templates, composability, cyber security, Deep Learning, deep learning algorithm, False Data Detection, false data injection attack, Gated Recurrent Unit (GRU), Human Behavior, Logic gates, machine learning algorithms, Power system measurements, pubcrawl, resilience, Resiliency, smoothing methods, Super-vised/Unsupervised Learning Detection Method, supervised learning, Training
AbstractIn recent years, cyber attackers are targeting the power system and imposing different damages to the national economy and public safety. False Data Injection Attack (FDIA) is one of the main types of Cyber-Physical attacks that adversaries can manipulate power system measurements and modify system data. Consequently, it may result in incorrect decision-making and control operations and lead to devastating effects. In this paper, we propose a two-stage detection method. In the first step, Gated Recurrent Unit (GRU), as a deep learning algorithm, is employed to forecast the data for the future horizon. Meanwhile, hyperparameter optimization is implemented to find the optimum parameters (i.e., number of layers, epoch, batch size, v1, v2, etc.) in the supervised learning process. In the second step, an unsupervised scoring algorithm is employed to find the sequences of false data. Furthermore, two penalty factors are defined to prevent the objective function from greedy behavior. We assess the capability of the proposed false data detection method through simulation studies on a real-world data set (ComEd. dataset, Northern Illinois, USA). The results demonstrate that the proposed method can detect different types of attacks, i.e., scaling, simple ramp, professional ramp, and random attacks, with good performance metrics (i.e., recall, precision, F1 Score). Furthermore, the proposed deep learning method can mitigate false data with the estimated true values.
DOI10.1109/PESGM46819.2021.9637951
Citation Keyparizad_semi-supervised_2021