A Survey on Explainable Artificial Intelligence for Internet Traffic Classification and Prediction, and Intrusion Detection
Alfredo Nascita, Giuseppe Aceto, Domenico Ciuonzo, Antonio Montieri, Valerio Persico, Antonio Pescapè
Abstract
With the increasing complexity and scale of modern networks, the demand for transparent and interpretable Artificial Intelligence (AI) models has surged. This survey comprehensively reviews the current state of eXplainable Artificial Intelligence (XAI) methodologies in the context of Network Traffic Analysis (NTA) (including tasks such as traffic classification, intrusion detection, attack classification, and traffic prediction), encompassing various aspects such as techniques, applications, requirements, challenges, and ongoing projects. It explores the vital role of XAI in enhancing network security, performance optimization, and reliability. Additionally, this survey underscores the importance of understanding why AI-driven decisions are made, emphasizing the need for explainability in critical network environments. By providing a holistic perspective on XAI for Internet NTA, this survey aims to guide researchers and practitioners in harnessing the potential of transparent AI models to address the intricate challenges of modern network management and security.