Fine-Grained Webpage Fingerprinting Using only Packet Length Information of Encrypted Traffic

Meng Shen, Yiting Liu, Liehuang Zhu*, Xiaojiang Du, Jiankun Hu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

72 Citations (Scopus)

Abstract

Encrypted web traffic can reveal sensitive information of users, such as their browsing behaviors. Existing studies on encrypted traffic analysis focus on website fingerprinting. We claim that fine-grained webpage fingerprinting, which speculates specific webpages on a same website visited by a victim, allows exploiting more user private information, e.g., shopping interests in an online shopping mall. Since webpages from the same website usually have very similar traffic traces that make them indistinguishable, existing solutions may end up with low accuracy. In this paper, we propose FineWP, a novel fine-grained webpage fingerprinting method. We make an observation that the length information of packets in bidirectional client-server interactions can be distinctive features for webpage fingerprinting. The extracted features are then fed into traditional machine learning models to train classifiers, which achieve both high accuracy and low training overhead. We collect two real-world traffic datasets and construct closed- and open-world evaluations to verify the effectiveness of FineWP. The experimental results demonstrate that FineWP is superior to the state-of-the-art methods in terms of accuracy, time complexity and stability.

Original languageEnglish
Article number9305740
Pages (from-to)2046-2059
Number of pages14
JournalIEEE Transactions on Information Forensics and Security
Volume16
DOIs
Publication statusPublished - 2021

Keywords

  • Webpage fingerprinting
  • convolutional neural networks
  • encrypted traffic classification
  • machine learning

Fingerprint

Dive into the research topics of 'Fine-Grained Webpage Fingerprinting Using only Packet Length Information of Encrypted Traffic'. Together they form a unique fingerprint.

Cite this