Deep Learning-Based PMU Cyber Security Scheme Against Data Manipulation Attacks With WADC Application

Mohamed Elimam, Younes J. Isbeih, Syafiq Kamarul Azman, Mohamed Shawky ElMoursi, Khalifa Al Hosani

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


In this paper, a new deep learning-based cyber-security scheme is proposed to detect PMU data manipulation attacks and to recover the corrupted measurements. The scheme consists of two deep learning models, an attack detection model, and a data recovery model. The first model can detect attack existence, and also determine which PMUs are attacked. The second model is used to reconstruct the manipulated signals and send them to the wide-area monitoring and control (WAMC) system. Unlike many approaches presented in the literature, the proposed models are capable of understanding the behavior of the system in both steady-state and transient operation, and therefore distinguishing between the normal and corrupted measurements. The state-of-art deep learning techniques are used to train and test these models. Furthermore, the effectiveness of the proposed cyber-security scheme is demonstrated for a wide-area damping control application. The obtained results show superior performance when the proposed model is evaluated using the IEEE 39-bus system and the 68-bus system. The security scheme succeeds to detect cyber-attacks with very high accuracy and recover the manipulated signals with a small reconstruction error.

Original languageBritish English
Pages (from-to)1-15
Number of pages15
JournalIEEE Transactions on Power Systems
StateAccepted/In press - 2022


  • Computer crime
  • Cyber-security
  • Data models
  • deep learning
  • Phasor measurement units
  • PMU
  • Power measurement
  • Power system stability
  • Security
  • Voltage measurement
  • wide-area damping control


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