Two-tier data-driven intrusion detection for automatic generation control in smart grid

Muhammad Qasim Ali, Reza Yousefian, Ehab Al-Shaer, Sukumar Kamalasadan, Quanyan Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Legacy energy infrastructures are being replaced by modern smart grids. Smart grids provide bi-directional communications for the purpose of efficient energy and load management. In addition, energy generation is adjusted based on the load feedback. However, due to the dependency on the cyber infrastructure for load monitoring and reporting, generation control is inherently vulnerable to attacks. Recent studies have shown that the possibility of data integrity attacks on the generation control can significantly disrupt the energy system. In this work, we present simple yet effective data-driven two-tier intrusion detection system for automatic generation control (AGC). The first tier is a short-term adaptive predictor for system variables, such as load and area control error (ACE). The first tier provides a real-time measurement predictor that adapts to the underlying changing behavior of these system variables, and flags out the abnormal behavior in these variables independently. The second tier provides deep state inspection to investigate the presence of anomalies by incorporating the overall system variable correlation using Markov models. Moreover, we expand our second tier inspection to include multi-AGC environment where a behavior of one AGC is validated against the behavior of the interconnected AGC. The combination of tier-1 light-weight prediction and tier-2 offline deep state inspection offers a great advantage to balance accuracy and real-time requirements of intrusion detection for AGC environment. Our results show high detection accuracy (95%) under different multi-attack scenarios. Second tier successfully verified all the injected intrusions.

Original languageEnglish (US)
Title of host publication2014 IEEE Conference on Communications and Network Security, CNS 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages292-300
Number of pages9
ISBN (Electronic)9781479958900
DOIs
StatePublished - Dec 23 2014
Event2014 IEEE Conference on Communications and Network Security, CNS 2014 - San Francisco, United States
Duration: Oct 29 2014Oct 31 2014

Publication series

Name2014 IEEE Conference on Communications and Network Security, CNS 2014

Other

Other2014 IEEE Conference on Communications and Network Security, CNS 2014
CountryUnited States
CitySan Francisco
Period10/29/1410/31/14

ASJC Scopus subject areas

  • Computer Networks and Communications

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