Abstract

Machine learning is increasingly applied to network traffic analysis to aid in tasks such as quality of service management, trend monitoring, and security. Recent advances in deep learning have enabled not only the classification of encrypted transits, but classification on a per-packet level. End-to-end deep learning models are becoming increasingly ubiq-uitous given their ease of use, i.e., developers do not need to engineer features, and their apparent versatility. However, deep learning entails black-box models that hinder the capability to debug and explain classifications. Moreover, the computational complexity of deep learning can incur unnecessary latency, which is problematic for real-time classification needs. In this paper, we propose a methodology to interpret black-box, deep learning-based encrypted network traffic classification models, with an attempt to understand the dominant features a classifier is …

Date

July 18, 2022

Authors

Luis Garcia, Genevieve Bartlett, Srivatsan Ravi, Harun Ibrahim, Wes Hardaker, Erik Kline

Conference

2022 IEEE International Symposium on Measurements & Networking (M&N)

Pages

1-6

Publisher

IEEE