Situational awareness through security-based analysis of controllability and observability in power grids

Asiri, Mohammed, Saxena, Neetesh ORCID: https://orcid.org/0000-0002-6437-0807 and Lakshminarayana, Subhash 2027. Situational awareness through security-based analysis of controllability and observability in power grids. Computers and Security 172 , 105156. 10.1016/j.cose.2026.105156

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Abstract

The integration of cyber and physical components in modern power grids has introduced vulnerabilities that challenge operators’ situational awareness during cyber attacks. Adversarial actions can compromise the grid’s controllability, defined as the ability to steer system states through control inputs, and observability, the ability to infer those states from sensor data. This paper addresses the lack of quantitative tools for assessing how Indicators of Compromise (IoCs) affect these key properties. We propose a comprehensive framework that evaluates the operational impact of cyber–physical attacks by quantifying how IoCs degrade system controllability and observability. Using a co-simulation environment, we simulate three coordinated attack scenarios: False Command Injection (FCI), Load Redistribution (LR), and False Data Injection (FDI). The framework introduces novel quantitative indices that capture both structural and operational degradation in controllability and observability capabilities. Our results reveal that coordinated attacks systematically exploit the interdependencies between cyber and physical layers, causing cascading effects that compromise both controllability and observability simultaneously. In the FCI case, the system experiences a 25.8% drop in controllability and a 32.6% reduction in observability redundancy, with cascading effects on voltage stability and power flow regulation. The LR scenario causes a 13.4% decline in controllability, primarily through operational stress redistribution, while preserving structural connectivity. The false data injection attack conceals physical disruptions, leading to a 33.5% drop in observability margin and masking critical state changes. The proposed indexes successfully quantify system degradation patterns that remain undetected by conventional security mechanisms, revealing compound vulnerabilities where the loss of control coincides with diminished situational awareness.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Schools > Computational & Mathematical Sciences Schools > Computer Science & Informatics
Publisher:	Elsevier
ISSN:	0167-4048
Date of First Compliant Deposit:	29 September 2026
Date of Acceptance:	5 September 2026
Last Modified:	29 Sep 2026 08:45
URI:	https://orca.cardiff.ac.uk/id/eprint/189857

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