Cybersecurity Risk Assessment and Detection in Smart Grid Infrastructure
DOI:
https://doi.org/10.22399/ijcesen.5556Keywords:
Smart Grid Security, Cybersecurity Risk Detection, False Data Injection Attack, SCADA Security, Power System Protection, Machine Learning for Smart GridsAbstract
Smart grids represent the next generation of power systems where electricity networks are integrated with digital communication technologies, distributed energy resources, and intelligent monitoring devices. The adoption of Advanced Metering Infrastructure (AMI), Supervisory Control and Data Acquisition (SCADA) systems, phasor measurement units (PMUs), and Internet-of-Things (IoT) sensors enables utilities to monitor and control power systems in real time. While these technologies significantly improve operational efficiency and grid reliability, they also introduce new cybersecurity vulnerabilities that can threaten the stability of critical energy infrastructure. Cyber-attacks such as false data injection attacks, distributed denial-of-service (DDoS) attacks, coordinated control attacks, and stealthy command manipulation in SCADA environments can disrupt grid operations, cause large-scale outages, and compromise sensitive operational data.This paper proposes an artificial intelligence–driven cybersecurity risk detection framework designed for modern smart grid infrastructures. The proposed framework integrates data from multiple grid components including SCADA systems, smart meters, PMUs, and network communication logs. A hybrid detection approach combining machine learning–based anomaly detection with graph-based threat propagation analysis is developed to identify abnormal operational patterns and potential cyber intrusion events. The framework further incorporates probabilistic risk propagation modeling to estimate the potential cascading impact of detected threats across interconnected grid components.Experimental validation using benchmark power system datasets demonstrates that the proposed approach significantly improves detection accuracy and reduces false alarm rates compared to traditional signature-based intrusion detection systems. The proposed framework supports real-time monitoring and provides utilities with a proactive mechanism for identifying cybersecurity threats before they escalate into major operational disruptions. This research contributes toward strengthening the resilience and security of future cyber-physical energy systems.
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