A deep learning anomaly detection framework with explainability and robustness
Manh-Dung Nguyen, Anis Bouaziz, Valeria Valdés, Ana Rosa Cavalli, Wissam Mallouli, Edgardo Montes de · 2023
The prevalence of encrypted Internet traffic has resulted in a pressing need for advanced analysis techniques for traffic analysis and classification. Traditional rule-based and signature-based approaches have been hindered by the introduction of network encryption methods. With the emergence of machine learning (ML) and deep learning (DL), several preliminary works have been developed for anomaly detection in encrypted network traffic. However, complex Artificial Intelligence (AI) models like neural networks lack explainability, limiting the understanding of their predictions. To address this limitation, eXplainable Artificial Intelligence (XAI) has emerged, aiming to provide users with a rationale for understanding AI system outputs and fostering trust. However, existing explainable frameworks still lack comprehensive support for adversarial attacks and defenses.