PREDICTIVE NEURAL NETWORK MODELS FOR CYBERATTACK PATTERN RECOGNITION AND CRITICAL INFRASTRUCTURE VULNERABILITY ASSESSMENT

Abstract

This study investigated the effectiveness of predictive neural network models in enhancing cyberattack detection and vulnerability assessment within critical infrastructure systems, addressing the limitations of traditional machine learning approaches in accuracy, adaptability, and operational performance. Drawing on a comprehensive review of 176 peer-reviewed studies published between 2015 and 2025, the research synthesized current advancements in machine learning, deep learning, and vulnerability analysis to develop and evaluate an integrated predictive framework. The empirical analysis was conducted on a large-scale, real-world dataset consisting of over 30 million network flow records, 12 million authentication and identity events, and more than 10,000 documented vulnerabilities from the energy, healthcare, and transportation sectors. The study employed convolutional neural networks (CNNs), gated recurrent units (GRUs), and hybrid CNN–GRU models, benchmarking them against logistic regression and random forest classifiers to measure improvements in detection accuracy, false positive reduction, vulnerability prioritization, and real-time performance. Findings revealed that neural network models consistently outperformed classical baselines, achieving AUC scores between 0.91 and 0.95 (compared to 0.84–0.87), reducing false positive rates by up to 38%, and improving precision by 12–17 percentage points at a recall of 0.90. Additionally, vulnerability prioritization accuracy improved substantially, with a 22–26% increase in top-100 exploited vulnerability hit rates and correlation coefficients above 0.86 with real-world exploitation events. Latency and throughput metrics demonstrated that CNN detectors processed samples in under 2 milliseconds, while hybrid models achieved event processing in less than 20 milliseconds, confirming their suitability for operational deployment. The study concludes that predictive neural network models offer a significant advancement in cybersecurity by capturing nonlinear relationships, modelling IT–OT dependencies, and integrating attack detection with vulnerability prioritization. These results extend the existing literature by providing a unified, scalable, and proactive defence framework for protecting critical infrastructure from evolving cyber threats and demonstrate the transformative potential of deep learning in the next generation of cybersecurity systems.