TONet: A Fast and Efficient Method for Traffic Obfuscation Using Adversarial Machine Learning

Fan Yang, Bingyang Wen, Cristina Comaniciu, K. P. Subbalakshmi, R. Chandramouli

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

In this letter, we address the problem of privacy leakage in communications based on analysis of traffic patterns. We propose an efficient method of traffic obfuscation based on neural networks, that generates traffic distortions with minimal overhead and computational cost. Our experimental results show that the proposed method is orders of magnitude faster in implementation and has a higher obfuscation success rate with less perturbation on the traffic samples, compared to previously proposed adversarial machine learning-based traffic obfuscation methods.

Original languageEnglish
Pages (from-to)2537-2541
Number of pages5
JournalIEEE Communications Letters
Volume26
Issue number11
DOIs
StatePublished - 1 Nov 2022

Keywords

  • Traffic type obfuscation
  • adversarial learning
  • privacy leakage
  • security

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