Robust Classification of Encrypted Network Services Using Convolutional Neural Networks Optimized by Information Bottleneck Method

The rapid growth of encrypted network traffic poses significant challenges for network monitoring and security systems, as traditional methods often fail to accurately classify encrypted services. This paper proposes a novel approach to achieve robust classification of encrypted network services using Convolutional Neural Networks (CNNs) optimized by the Information Bottleneck (IB) method (IB-CNN). The IB method is employed to reduce the complexity of the model while retaining the most relevant information for classification, thereby improving its performance and generalization capabilities. We design and implement a CNN architecture tailored to the characteristics of encrypted network traffic, and apply the IB method to optimize the intermediate representations. Extensive experiments on a diverse dataset of encrypted network services demonstrate that our approach significantly outperforms existing methods in terms of accuracy and robustness, even in the presence of varying network conditions and adversarial attacks. Additionally, we analyze the impact of different IB parameters on the classification performance and provide insights into the optimal configuration for practical deployment. This study contributes to advancing the state-of-the-art in encrypted network service classification and offers a promising direction for enhancing the security and reliability of network monitoring systems.