Explainable AI-Based Cyber Threat Detection Framework Using LSTM and Autoencoder for Intelligent Network Security

Abstract

The rapid advancement of digital communication and networking technologies has resulted in a significant increase in the frequency and sophistication of cyber threats, creating new challenges for securing modern computer networks. Traditional intrusion detection approaches mainly depend on signature-based techniques and predefined security rules, making them less effective against newly emerging and continuously evolving cyber attacks. To address these limitations, this study presents an Explainable Artificial Intelligence (XAI)-based cyber threat detection framework that combines Long Short-Term Memory (LSTM) and Autoencoder models for accurate and transparent threat detection. The LSTM model captures sequential network traffic patterns to identify temporal attack behaviours, whereas the Autoencoder detects anomalous activities by learning the characteristics of normal network traffic. The integration of Explainable AI enables users to understand prediction outcomes by highlighting the factors influencing each detection decision. Furthermore, the framework performs quantitative risk assessment, categorizes threats into multiple severity levels, and generates suitable mitigation recommendations through an interactive web-based dashboard. Experimental results demonstrate that the proposed framework provides reliable, interpretable, and effective cyber threat detection, thereby supporting security professionals in making timely and well-informed decisions.