Scholarly record
RENEWABLE ENERGY INFRASTRUCTURE: VULNERABILITY MAPPING AND PREDICTIVE RISK MODELING
Abstract
Industrial Control Systems (ICS) are foundational to critical national infrastructure, operating vital processes in sectors such as water treatment, power generation, oil and gas, and manufacturing. As these systems become increasingly integrated with information technology through the Industrial Internet of Things (IIoT), they face growing risks from cyber threats. As Bulgaria accelerates the integration of renewable energy infrastructure, particularly solar, wind, and gas-to-power systems, the cybersecurity of these digital assets becomes a national imperative. This study presents a cybersecurity risk modeling framework for renewable energy systems, combining open-source intelligence (OSINT), port and protocol analysis, and advanced machine learning techniques. Traditional security approaches, which focus on signature-based detection, have proven inadequate in the face of sophisticated, stealthy, or zero-day attacks. To address this, our research proposes a data-driven framework for detecting anomalies in ICS environments using time-series prediction models. Specifically, we evaluate CNN-LSTM hybrids, LSTM Autoencoders, and Isolation Forests across two widely used datasets-SWaT (Secure Water Treatment) and BATADAL (Battle of the Attack Detection Algorithms). This report presents a comprehensive comparison of these techniques, analyzing their detection accuracy, cross-domain robustness, and operational feasibility. Our findings demonstrate that hybrid deep learning models can effectively identify cyber-physical anomalies in ICS telemetry data, although domain-specific calibration remains crucial for optimal performance.
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References9
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Ngoc Le, S. Anomaly Detection with SWaT Dataset. GitHub Repository, 2023.
Chen, S.Y. A Survey on BATADAL Dataset. GitHub Repository, 2023.
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Number of times cited according to Crossref: 1
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