Modeling and Mitigating Impact of False Data Injection Attacks on Automatic Generation Control

Rui Tan, Hoang Hai Nguyen, Eddy Y.S. Foo, David K.Y. Yau, Zbigniew Kalbarczyk, Ravishankar K. Iyer, Hoay Beng Gooi

Research output: Contribution to journalArticlepeer-review


This paper studies the impact of false data injection (FDI) attacks on automatic generation control (AGC), a fundamental control system used in all power grids to maintain the grid frequency at a nominal value. Attacks on the sensor measurements for AGC can cause frequency excursion that triggers remedial actions, such as disconnecting customer loads or generators, leading to blackouts, and potentially costly equipment damage. We derive an attack impact model and analyze an optimal attack, consisting of a series of FDIs that minimizes the remaining time until the onset of disruptive remedial actions, leaving the shortest time for the grid to counteract. We show that, based on eavesdropped sensor data and a few feasible-to-obtain system constants, the attacker can learn the attack impact model and achieve the optimal attack in practice. This paper provides essential understanding on the limits of physical impact of the FDIs on power grids, and provides an analysis framework to guide the protection of sensor data links. For countermeasures, we develop efficient algorithms to detect the attack, estimate which sensor data links are under attack, and mitigate attack impact. Our analysis and algorithms are validated by experiments on a physical 16-bus power system test bed and extensive simulations based on a 37-bus power system model.

Original languageEnglish (US)
Article number7867825
Pages (from-to)1609-1624
Number of pages16
JournalIEEE Transactions on Information Forensics and Security
Issue number7
StatePublished - Jul 2017


  • Power grid
  • automatic generation control
  • cyber security
  • false data injection

ASJC Scopus subject areas

  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications


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