Visible to the public Training Strategies for Autoencoder-based Detection of False Data Injection Attacks

TitleTraining Strategies for Autoencoder-based Detection of False Data Injection Attacks
Publication TypeConference Paper
Year of Publication2020
AuthorsWang, Chenguang, Pan, Kaikai, Tindemans, Simon, Palensky, Peter
Conference Name2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe)
Date PublishedOct. 2020
ISBN Number978-1-7281-7100-5
Keywordsanomaly detection, autoencoder, composability, cyber physical systems, False Data Detection, false data injection attack, Human Behavior, human factors, hyperparameter tuning, Load flow, Neural networks, pubcrawl, resilience, Resiliency, Robustness, security, Smart grids, Training, Tuning
AbstractThe security of energy supply in a power grid critically depends on the ability to accurately estimate the state of the system. However, manipulated power flow measurements can potentially hide overloads and bypass the bad data detection scheme to interfere the validity of estimated states. In this paper, we use an autoencoder neural network to detect anomalous system states and investigate the impact of hyperparameters on the detection performance for false data injection attacks that target power flows. Experimental results on the IEEE 118 bus system indicate that the proposed mechanism has the ability to achieve satisfactory learning efficiency and detection accuracy.
Citation Keywang_training_2020