Adapting Topic Modeling for Network Behavior Analysis

The ever-increasing diversity of devices and the complexity of communication content pose significant challenges for network traffic analysis. These limitations can hinder anomaly detection and network control, both essential for guaranteeing stable communication in next-generation networks. This paper proposes an approach leveraging Latent Dirichlet Allocation (LDA) topic modeling for network traffic analysis. LDA, a powerful machine learning technique, excels at identifying hidden thematic structures within data. When applied to network traffic, LDA can differentiate traffic types, such as web browsing, email, or file sharing. To optimize the effectiveness of traffic demand capture, we conducted experiments to determine the appropriate window size for individual connections and the ideal number of topics needed for accurate traffic analysis. Our proposed topic estimation algorithm, utilizing the bag-of-words representation, demonstrates its efficacy in identifying multiple topics within real-world network traffic datasets. This proposed approach has the potential to significantly improve network performance, security, and management.