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Deep Fingerprinting: Undermining Website Fingerprinting Defenses with Deep Learning
Website fingerprinting enables a local eavesdropper to determine which w...
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Correlation between Content and Traffic of the Universities Website
The purpose of this study is to analyse the correlation between content ...
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Credibility of Automatic Appraisal of Domain Names
Both domain names and entire websites are increasingly frequently treate...
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A Quantitative Approach in Heuristic Evaluation of E-commerce Websites
This paper presents a pilot study on developing an instrument to predict...
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Var-CNN and DynaFlow: Improved Attacks and Defenses for Website Fingerprinting
In recent years, there have been many works that use website fingerprint...
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Fingerprinting the Fingerprinters: Learning to Detect Browser Fingerprinting Behaviors
Browser fingerprinting is an invasive and opaque stateless tracking tech...
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Leveraging the Flow of Collective Attention for Computational Communication Research
Human attention becomes an increasingly important resource for our under...
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Traffic Analysis with Deep Learning
Deep Neural Networks (DNN) has obtained enormous attention with its advantageous feature learning and its powerful prediction ability. In this paper, we broadly study the applicability of deep learning to traffic analysis and present its effectiveness on the feature extraction for state-of-the-art machine learning algorithms, website and keyword fingerprinting attacks, and the prediction on the fingerprintability of websites. To the best of our knowledge, this is the first extensive work to introduce various applications using DNN in traffic analysis. With great help of DNN, the quality of cutting edge website fingerprinting attacks is upgraded while the feature dimension becomes much lower. As the classifiers, DNN successfully detects which website the user visited among 100 websites with 91 background websites, and as the fingerprintability predictors, it almost perfectly determines the fingerprintability of 4,500 website traffic instances with 99
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