Website Fingerprinting at Internet Scale

By Andriy Panchenko, Fabian Lanze, Jan Pennekamp, Thomas Engel, Andreas Zinnen, Martin Henze, Klaus Wehrle. Network and Distributed System Security Symposium, 2016

657 citations recorded by the source corpus.

The website fingerprinting attack aims to identify the content (i.e., a webpage accessed by a client) of encrypted and anonymized connections by observing patterns of data flows such as packet size and direction. This attack can be performed by a local passive eavesdropper – one of the weakest adversaries in the attacker model of anonymization networks such as Tor. In this paper, we present a novel website fingerprinting attack. Based on a simple and comprehensible idea, our approach outperforms all state-of-the-art methods in terms of classification accuracy while being computationally dramatically more efficient. In order to evaluate the severity of the website fingerprinting attack in reality, we collected the most representative dataset that has ever been built, where we avoid simplified assumptions made in the related work regarding selection and type of webpages and the size of the universe. Using this data, we explore the practical limits of website fingerprinting at Internet scale. Although our novel approach is by orders of magnitude computationally more efficient and superior in terms of detection accuracy, for the first time we show that no existing method – including our own – scales when applied in realistic settings. With our analysis, we explore neglected aspects of the attack and investigate the realistic probability of success for different strategies a real-world adversary may follow.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/47daf2375f6e635198dfd025bf9c68477eed2850

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Citations in the corpus

k-fingerprinting: A Robust Scalable Website Fingerprinting Technique (2015, methodology): “Defenses This work k-NN [39] CUMUL [28] Bandwidth overhead (%)”
Automated Website Fingerprinting through Deep Learning (2017, methodology): “[24], and (2) the base rate fallacy, demonstrated by Juarez et al.”
ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification (2022, methodology): “We compare ET-BERT with various state-of-the-art (SOTA) methods, including (1) fingerprint construction method: FlowPrint [35]; (2) statistical featuremethods: AppScanner [34], CUMUL [25], BIND [1] and k-fingerprinting (K-fp) [11]; (3) deep learningmethods: Deep Fingerprinting (DF) [33], FS-Net [20]”
Accurate Decentralized Application Identification via Encrypted Traffic Analysis Using Graph Neural Networks (2021, methodology): “[17] leveraged the accumulated sum of packet lengths as features that were fed to a Support Vector Machine (SVM) classifier.”
Robust Smartphone App Identification via Encrypted Network Traffic Analysis (2017, background): “[17] present a website fingerprinting attack on Tor that outperforms related”
Deep Fingerprinting: Undermining Website Fingerprinting Defenses with Deep Learning (2018, methodology): “Previous WF attacks use a set of hand-crafted features to represent Tor traffic, achieving 90%+ accuracy against Tor using classifiers such as Support Vector Machine (SVM) [27], k -Nearest Neighbors ( k -NN) [38], and random forests [14].”