Next Article in Journal
GWS—A Collaborative Load-Balancing Algorithm for Internet-of-Things
Next Article in Special Issue
Multi-Robot Cyber Physical System for Sensing Environmental Variables of Transmission Line
Previous Article in Journal
Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms
Open AccessArticle

Physical-Model-Checking to Detect Switching-Related Attacks in Power Systems

Energy Systems Research Laboratory, Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA
Author to whom correspondence should be addressed.
Sensors 2018, 18(8), 2478;
Received: 7 June 2018 / Revised: 18 July 2018 / Accepted: 30 July 2018 / Published: 31 July 2018
(This article belongs to the Special Issue Smart Grid Networks and Energy Cyber Physical Systems)
Recent public disclosures on attacks targeting the power industry showed that savvy attackers are now capable of occulting themselves from conventional rule-based network intrusion detection systems (IDS), bringing about serious threats. In order to leverage the work of rule-based IDS, this paper presents an artificially intelligent physical-model-checking intrusion detection framework capable of detecting tampered-with control commands from control centers of power grids. Unlike the work presented in the literature, the work in this paper utilizes artificial intelligence (AI) to learn the load flow characteristics of the power system and benefits from the fast responses of the AI to decode and understand contents of network packets. The output of the AI is processed through an expert system to verify that incoming control commands do not violate the physical system operational constraints and do not put the power system in an insecure state. The proposed content-aware IDS is tested in simulation on a 14-bus IEEE benchmark system. Experimental verification on a small power system, with an IEC 61850 network architecture is also carried out. The results showed the accuracy of the proposed framework in successfully detecting malicious and/or erroneous control commands. View Full-Text
Keywords: agent systems; cyber-physical security; decentralized control; intelligent systems agent systems; cyber-physical security; decentralized control; intelligent systems
Show Figures

Figure 1

MDPI and ACS Style

El Hariri, M.; Faddel, S.; Mohammed, O. Physical-Model-Checking to Detect Switching-Related Attacks in Power Systems. Sensors 2018, 18, 2478.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop