EXPLAINABLE AI-BASED INTRUSION DETECTION FRAMEWORK FOR CRITICAL INFRASTRUCTURE PROTECTION IN PAKISTAN’S DIGITAL ECOSYSTEM

Abstract

The rapid expansion of digital infrastructure in Pakistan has increased the vulnerability of critical systems such as banking, healthcare, energy, telecommunications, and government services to sophisticated cyber threats. Traditional intrusion detection systems (IDS) are increasingly insufficient due to their limited adaptability, high false-positive rates, and inability to detect zero-day attacks. Although Artificial Intelligence (AI) and Machine Learning (ML)-based IDS models have improved detection accuracy, their “black-box” nature limits transparency, trust, and operational acceptance in critical infrastructure environments. To address these challenges, this study proposed an Explainable AI-Based Intrusion Detection Framework designed to enhance both cybersecurity performance and interpretability. The framework integrated advanced machine learning algorithms with Explainable Artificial Intelligence (XAI) techniques, including SHAP and LIME, to provide transparent and interpretable threat detection. The model was evaluated using benchmark datasets and expert assessments from cybersecurity professionals. Results demonstrated that the proposed framework achieved superior performance in terms of accuracy, precision, recall, and false-positive reduction compared to traditional models. Additionally, expert evaluations confirmed high levels of interpretability, transparency, and trust in AI-driven decisions. The findings highlight that integrating explainability into IDS frameworks significantly strengthens cybersecurity resilience and supports informed decision-making in critical infrastructure environments. The study concludes that XAI-based intrusion detection systems offer a reliable and scalable solution for protecting Pakistan’s evolving digital ecosystem