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Title: An Artificially Intelligent Physical Model-Checking Approach to Detect Switching-Related Attacks on Power Systems

Abstract

Decentralized and hierarchical microgrid control strategies have lain the groundwork for shaping the future smart grid. Such control approaches require the cooperation between microgrid operators in control centers, intelligent microcontrollers, and remote terminal units via secure and reliable communication networks. In order to enhance the security and complement the work of network intrusion detection systems, this paper presents an artificially intelligent physical model-checking that detects tampered-with circuit breaker switching control commands whether, due to a cyber-attack or human error. In this technique, distributed agents, which are monitoring sectionalized areas of a given microgrid, will be trained and continuously adapted to verify that incoming control commands do not violate the physical system operational standards and do not put the microgrid in an insecure state. The potential of this approach has been tested by deploying agents that monitor circuit breakers status commands on a 14-bus IEEE benchmark system. The results showed the accuracy of the proposed framework in characterizing the power system and successfully detecting malicious and/or erroneous control commands.

Authors:
 [1];  [1];  [1]
  1. Florida Intl Univ., Miami, FL (United States)
Publication Date:
Research Org.:
Florida International University
Sponsoring Org.:
USDOE Office of Electricity Delivery and Energy Reliability (OE)
OSTI Identifier:
1406124
DOE Contract Number:  
OE0000779
Resource Type:
Conference
Resource Relation:
Conference: IEEE ICPES Toronto Canda 2017
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; agent systems; cyber-physical security; decentralized control; intelligent systems; microgrid reliability

Citation Formats

El Hariri, Mohamad, Faddel, Samy, and Mohammed, Osama. An Artificially Intelligent Physical Model-Checking Approach to Detect Switching-Related Attacks on Power Systems. United States: N. p., 2017. Web. doi:10.1109/ICPESYS.2017.8215914.
El Hariri, Mohamad, Faddel, Samy, & Mohammed, Osama. An Artificially Intelligent Physical Model-Checking Approach to Detect Switching-Related Attacks on Power Systems. United States. doi:10.1109/ICPESYS.2017.8215914.
El Hariri, Mohamad, Faddel, Samy, and Mohammed, Osama. Wed . "An Artificially Intelligent Physical Model-Checking Approach to Detect Switching-Related Attacks on Power Systems". United States. doi:10.1109/ICPESYS.2017.8215914. https://www.osti.gov/servlets/purl/1406124.
@article{osti_1406124,
title = {An Artificially Intelligent Physical Model-Checking Approach to Detect Switching-Related Attacks on Power Systems},
author = {El Hariri, Mohamad and Faddel, Samy and Mohammed, Osama},
abstractNote = {Decentralized and hierarchical microgrid control strategies have lain the groundwork for shaping the future smart grid. Such control approaches require the cooperation between microgrid operators in control centers, intelligent microcontrollers, and remote terminal units via secure and reliable communication networks. In order to enhance the security and complement the work of network intrusion detection systems, this paper presents an artificially intelligent physical model-checking that detects tampered-with circuit breaker switching control commands whether, due to a cyber-attack or human error. In this technique, distributed agents, which are monitoring sectionalized areas of a given microgrid, will be trained and continuously adapted to verify that incoming control commands do not violate the physical system operational standards and do not put the microgrid in an insecure state. The potential of this approach has been tested by deploying agents that monitor circuit breakers status commands on a 14-bus IEEE benchmark system. The results showed the accuracy of the proposed framework in characterizing the power system and successfully detecting malicious and/or erroneous control commands.},
doi = {10.1109/ICPESYS.2017.8215914},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Wed Nov 01 00:00:00 EDT 2017},
month = {Wed Nov 01 00:00:00 EDT 2017}
}

Conference:
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