On Multi-Session Website Fingerprinting over TLS Handshake
Aida Ramezani, Amirhossein Khajehpour, Mahdi Jafari Siavoshani

TL;DR
This paper introduces a deep learning multi-label classifier that predicts websites visited during a period by analyzing server names in TLS handshake packets, demonstrating high accuracy and improved performance over simpler models.
Contribution
The paper presents a novel deep learning approach utilizing server name sequences in TLS handshakes for multi-session website fingerprinting, outperforming basic neural network models.
Findings
Achieved 95% accuracy on test data
Maintained above 90% accuracy on modified and human-made datasets
Using sequential information improves classification performance
Abstract
Analyzing users' Internet traffic data and activities has a certain impact on users' experiences in different ways, from maintaining the quality of service on the Internet and providing users with high-quality recommendation systems to anomaly detection and secure connection. Considering that the Internet is a complex network, we cannot disintegrate the packets for each activity. Therefore we have to have a model that can identify all the activities an Internet user does in a given period of time. In this paper, we propose a deep learning approach to generate a multi-label classifier that can predict the websites visited by a user in a certain period. This model works by extracting the server names appearing in chronological order in the TLSv1.2 and TLSv1.3 Client Hello packets. We compare the results on the test data with a simple fully-connected neural network developed for the same…
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Taxonomy
TopicsInternet Traffic Analysis and Secure E-voting · Network Security and Intrusion Detection · Spam and Phishing Detection
