Towards Explainable and Lightweight AI for Real-Time Cyber Threat Hunting in Edge Networks

Abstract

As cyber threats continue to evolve, securing edge networks has become increasingly challenging due to their distributed nature and resource limitations. Many AI-driven threat detection systems rely on complex deep learning models, which, despite their high accuracy, suffer from two major drawbacks: lack of interpretability and high computational cost. Black-box AI models make it difficult for security analysts to understand the reasoning behind their predictions, limiting their practical deployment. Moreover, conventional deep learning techniques demand significant computational resources, rendering them unsuitable for edge devices with limited processing power. To address these issues, this study introduces an Explainable and Lightweight AI (ELAI) framework designed for real-time cyber threat detection in edge networks. Our approach integrates interpretable machine learning algorithms with optimized lightweight deep learning techniques, ensuring both transparency and computational efficiency. The proposed system leverages decision trees, attention-based deep learning, and federated learning to enhance detection accuracy while maintaining explainability. We evaluate ELAI using benchmark cybersecurity datasets, such as CICIDS and UNSW-NB15, assessing its performance across diverse cyberattack scenarios. Experimental results demonstrate that the proposed framework achieves high detection rates with minimal false positives, all while significantly reducing computational demands compared to traditional deep learning methods. The key contributions of this work include: (1) a novel interpretable AI-based cybersecurity model tailored for edge computing environments, (2) an optimized lightweight deep learning approach for real-time cyber threat detection, and (3) a comprehensive analysis of explainability techniques in AI-driven cybersecurity applications.