Robust Network Traffic Classification Based on Information Bottleneck Neural Network

Network Traffic Classification (NTC) plays a critical role in modern network security and management, used in various applications such as performance monitoring, anomaly detection, and bandwidth management. Traditional methods like port-based identification and deep packet inspection (DPI) face limitations due to evolving encryption techniques and dynamic port usage. Recent advances in machine learning have improved NTC but often struggle with data labeling, encrypted traffic, and model complexity. This paper introduces a novel Information Bottleneck Neural Network (IBNN) to address these challenges by efficiently extracting essential features from raw traffic data while minimizing irrelevant information. The IBNN framework balances model complexity and discriminative feature preservation, leading to enhanced classification accuracy and robustness, particularly for encrypted traffic. Through information-centric feature extraction and relevant information compression, the proposed method outperforms existing techniques about 5–7% in terms of accuracy, as well as F1 score on both ISCX 2012 and UNB CIS datasets, delivering superior resilience and adaptability across diverse network scenarios. Extensive experiments demonstrate the efficacy of IBNN, positioning it as a significant advancement in the field of NTC.