MPAF: Encrypted Traffic Classification With Multi-Phase Attribute Fingerprint
Yige Chen, Yipeng Wang
Abstract
The widespread use of cryptographic protocols such as Transport Layer Security (TLS) has necessitated the development of effective methods for encrypted traffic classification. The existing methods relying on a single feature source face challenges in achieving high accuracy and efficiency simultaneously. Additionally, there is a decrease in accuracy in complex scenarios, posing significant challenges for networks and security services based on application-level traffic classification. In this paper, we propose Multi-Phase Attribute Fingerprint (MPAF), an encrypted traffic classification system that overcomes these limitations. MPAF leverages three phases to separately leverage attributes that emerge at different time periods of encrypted traffic communication. Additionally, we transform discrete attributes into computable vectors through embedding and design a classifier for the multi-phase mechanism based on a leaf node masking tree. The experimental results show that MPAF achieves a classification accuracy ranging from 96.33% to 99.42% and an average waiting time (AWT) ranging from 0.18s to 0.45s. MPAF outperforms other approaches in scenarios with high robustness requirements, including small-scale training datasets, cross-dataset classification, and unknown application recognition.