AI-DRIVEN CYBER THREAT INTELLIGENCE FOR CRITICAL INFRASTRUCTURE PROTECTION IN PAKISTAN: A DEEP LEARNING APPROACH

Abstract

The increasing digitization of critical infrastructure systems in Pakistan has significantly expanded the attack surface for sophisticated cyber threats, including advanced persistent threats, ransomware, and zero-day exploits. Traditional rule-based cybersecurity mechanisms are increasingly insufficient to address these evolving and complex threats. This study proposes an AI-driven Cyber Threat Intelligence (CTI) framework based on deep learning techniques to enhance the protection of critical infrastructure. The proposed model integrates Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Autoencoders to enable real-time anomaly detection, threat classification, and predictive analysis. A quantitative experimental design was employed using benchmark cybersecurity datasets and simulated critical infrastructure environments. The results demonstrate that the hybrid deep learning model outperforms traditional machine learning and signature-based approaches, achieving higher detection accuracy and lower false positive rates. The findings confirm that AI-based CTI significantly improves cybersecurity resilience, enabling proactive threat mitigation in high-risk environments. The study contributes to advancing intelligent cybersecurity frameworks and provides practical implications for strengthening national cyber defense systems in Pakistan.