GANDaLF: GAN for Data-Limited Fingerprinting

Abstract We introduce Generative Adversarial Networks for Data-Limited Fingerprinting (GANDaLF), a new deep-learning-based technique to perform Website Fingerprinting (WF) on Tor traffic. In contrast to most earlier work on deep-learning for WF, GANDaLF is intended to work with few training samples, and achieves this goal through the use of a Generative Adversarial Network to generate a large set of “fake” data that helps to train a deep neural network in distinguishing between classes of actual training data. We evaluate GANDaLF in low-data scenarios including as few as 10 training instances per site, and in multiple settings, including fingerprinting of website index pages and fingerprinting of non-index pages within a site. GANDaLF achieves closed-world accuracy of 87% with just 20 instances per site (and 100 sites) in standard WF settings. In particular, GANDaLF can outperform Var-CNN and Triplet Fingerprinting (TF) across all settings in subpage fingerprinting. For example, GANDaLF outperforms TF by a 29% margin and Var-CNN by 38% for training sets using 20 instances per site.

[1]  Srinivas Devadas,et al.  Var-CNN: A Data-Efficient Website Fingerprinting Attack Based on Deep Learning , 2018, Proc. Priv. Enhancing Technol..

[2]  Hannes Federrath,et al.  Website fingerprinting: attacking popular privacy enhancing technologies with the multinomial naïve-bayes classifier , 2009, CCSW '09.

[3]  George Danezis,et al.  k-fingerprinting: A Robust Scalable Website Fingerprinting Technique , 2015, USENIX Security Symposium.

[4]  Micah Sherr,et al.  Understanding Tor Usage with Privacy-Preserving Measurement , 2018, Internet Measurement Conference.

[5]  Klaus Wehrle,et al.  Website Fingerprinting at Internet Scale , 2016, NDSS.

[6]  Sepp Hochreiter,et al.  GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium , 2017, NIPS.

[7]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[8]  Mohsen Imani,et al.  Deep Fingerprinting: Undermining Website Fingerprinting Defenses with Deep Learning , 2018, CCS.

[9]  Tao Wang,et al.  Effective Attacks and Provable Defenses for Website Fingerprinting , 2014, USENIX Security Symposium.

[10]  Mohammad Saidur Rahman,et al.  Triplet Fingerprinting: More Practical and Portable Website Fingerprinting with N-shot Learning , 2019, CCS.

[11]  Claudia Díaz,et al.  Inside Job: Applying Traffic Analysis to Measure Tor from Within , 2018, NDSS.

[12]  Soumith Chintala,et al.  Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.

[13]  Tao Wang,et al.  Walkie-Talkie: An Efficient Defense Against Passive Website Fingerprinting Attacks , 2017, USENIX Security Symposium.

[14]  Nicholas Hopper,et al.  p1-FP: Extraction, Classification, and Prediction of Website Fingerprints with Deep Learning , 2019, Proc. Priv. Enhancing Technol..

[15]  Mike Perry,et al.  Toward an Efficient Website Fingerprinting Defense , 2015, ESORICS.

[16]  Wouter Joosen,et al.  Automated Website Fingerprinting through Deep Learning , 2017, NDSS.

[17]  Rachel Greenstadt,et al.  A Critical Evaluation of Website Fingerprinting Attacks , 2014, CCS.

[18]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[19]  Tim Salimans,et al.  Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks , 2016, NIPS.

[20]  Chuan Sheng Foo,et al.  Semi-Supervised Learning with GANs: Revisiting Manifold Regularization , 2018, ICLR.

[21]  Jost Tobias Springenberg,et al.  Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks , 2015, ICLR.

[22]  Mohammad Saidur Rahman,et al.  Tik-Tok: The Utility of Packet Timing in Website Fingerprinting Attacks , 2019, Proc. Priv. Enhancing Technol..

[23]  Shuai Li,et al.  Fingerprinting Keywords in Search Queries over Tor , 2017, Proc. Priv. Enhancing Technol..

[24]  Wojciech Zaremba,et al.  Improved Techniques for Training GANs , 2016, NIPS.

[25]  Thomas Engel,et al.  Website fingerprinting in onion routing based anonymization networks , 2011, WPES.

[26]  Oriol Vinyals,et al.  Towards Principled Unsupervised Learning , 2015, ArXiv.