| Title | Detection of False Data Injection Attacks Using Cross Wavelet Transform and Machine Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Hakim, Mohammad Sadegh Seyyed, Karegar, Hossein Kazemi |
| Conference Name | 2021 11th Smart Grid Conference (SGC) |
| Keywords | command injection attacks, composability, Computational modeling, Cyber-physical security, false data injection attack, feature extraction, machine learning, Metrics, Power measurement, pubcrawl, Resiliency, Smart grids, state estimation, Time measurement, Time-frequency Analysis, wavelet transform, wavelet transforms |
| Abstract | Power grids are the most extensive man-made systems that are difficult to control and monitor. With the development of conventional power grids and moving toward smart grids, power systems have undergone vast changes since they use the Internet to transmit information and control commands to different parts of the power system. Due to the use of the Internet as a basic infrastructure for smart grids, attackers can sabotage the communication networks and alter the measurements. Due to the complexity of the smart grids, it is difficult for the network operator to detect such cyber-attacks. The attackers can implement the attack in a manner that conventional Bad Data detection (BDD) systems cannot detect since it may not violate the physical laws of the power system. This paper uses the cross wavelet transform (XWT) to detect stealth false data injections attacks (FDIAs) against state estimation (SE) systems. XWT can capture the coherency between measurements of adjacent buses and represent it in time and frequency space. Then, we train a machine learning classification algorithm to distinguish attacked measurements from normal measurements by applying a feature extraction technique. |
| DOI | 10.1109/SGC54087.2021.9664053 |
| Citation Key | hakim_detection_2021 |
Detection of False Data Injection Attacks Using Cross Wavelet Transform and Machine Learning