The Role of Deep Learning in Network Security using Explainable Artificial Intelligence

Abstract

Deep learning (DL) methods have advanced network security capabilities across intrusion detection, malware detection, traffic classification, and threat hunting by learning complex patterns from high-dimensional data. However, DL models are often black boxes, which limit operational adoption in Security Operations Centers (SOCs) where human analysts must trust, verify, and act on model outputs. Explainable AI (XAI) techniques bridge this gap by providing local and global explanations that increase transparency, enable model debugging, and improve analyst decision-making. This paper surveys DL applications in network security, reviews XAI methods adapted to cyber security, proposes an integrated XAI–DL framework for intrusion detection, reports an evaluation strategy, and discusses challenges and future directions.