Bits Learning: User-Adjustable Privacy Versus Accuracy in Internet Traffic Classification

@article{Yuan2016BitsLU,
  title={Bits Learning: User-Adjustable Privacy Versus Accuracy in Internet Traffic Classification},
  author={Zhenlong Yuan and Jie Xu and Yibo Xue and Mihaela van der Schaar},
  journal={IEEE Communications Letters},
  year={2016},
  volume={20},
  pages={704-707}
}
During the past decade, a great number of machine learning (ML)-based methods have been studied for accurate traffic classification. Flow features such as the discretizations of the first five packet sizes (PS) and flow ports (FP) are considered the best discriminators for per-flow classification. For the first time, this letter proposes to treat the first n-bits of a flow (BitFlow) as features and compares its overall performance with the well-known ACAS (automated construction of application… CONTINUE READING

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