Website fingerprinting in onion routing based anonymization networks

By Andriy Panchenko, Lukas Niessen, Andreas Zinnen, Thomas Engel. 2011

585 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

Proactively Accountable Anonymous Messaging in Verdict (2012, background): “Anonymizing relay tools, such as Tor [18], offer practical and scalable anonymous communication but are vulnerable to traffic analysis attacks [4, 34, 38] feasible for powerful adversaries, such as ISPs in authoritarian states.”
Improved website fingerprinting on Tor (2013, methodology): “Tor uses TLS to communicate between relays.”
Robust Smartphone App Identification via Encrypted Network Traffic Analysis (2017, methodology): “Similar work was proposed by Panchenko et al. [18].”
ET-BERT: A Contextualized Datagram Representation with Pre-training Transformers for Encrypted Traffic Classification (2022, methodology): “To transform a session flow into a token similar to a "word" in NLP, we propose three processes: (1) BURST Generator extracts the partial complete information of a session flow, namely the one-way continuous packets (BURST [28, 33]); (2) BURST2Token characterizes the datagram in each BURST intom tok”
A Systematic Approach to Developing and Evaluating Website Fingerprinting Defenses (2014, background): “In short, none of these defenses can be considered a perfect ﬁt for Tor.”
Circuit Fingerprinting Attacks: Passive Deanonymization of Tor Hidden Services (2015, methodology): “[33] improved the results by using a Support Vector Machine (SVM) classifier, using features that are mainly based on the volume, time, and direction of the traffic, and achieved more than 50% accuracy in a closedworld experiment of 775 URLs.”
Touching from a distance: website fingerprinting attacks and defenses (2012, methodology): “…that used randomized pipelining, padded all packets to Attack Defense Database Size Success Rate Our page ﬁngerprinting attack Tor 100 83.7% Ad hoc SVM [17] Tor 100 65.4% Cosine Similarity [19] Tor 20 50% Multinomial Naive-Bayes [10] Tor 100 4.4% Our page ﬁngerprinting attack Tor + rand. pipe.”
DupLESS: Server-Aided Encryption for Deduplicated Storage (2013, background): “We will not explicitly target resistance to traffic analysis attacks that abuse leakage of access patterns [48] or file lengths [24] [31] [40] [47] [59] [65] [72], though our system will be compatible with potential countermeasures.”
AppScanner: Automatic Fingerprinting of Smartphone Apps from Encrypted Network Traffic (2016, methodology): “Similar work was proposed by Panchenko et al. [13].”
Automated Website Fingerprinting through Deep Learning (2017, unknown): “The feasibility of Website Fingerprinting (WF) attacks on Tor was assessed in a series of studies [20], [25], [26], [31], [32].”
Deep Fingerprinting: Undermining Website Fingerprinting Defenses with Deep Learning (2018, background): “Prior work has shown that, under certain conditions, a local and passive adversary can identify the pages visited by a Tor user by exploiting patterns in network traffic [8, 14, 16, 27, 28, 39, 40].”
k-fingerprinting: A Robust Scalable Website Fingerprinting Technique (2015, methodology): “The total number of packets, along with the number of incoming and outgoing packets [12, 27, 39].”
MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples (2019, background): “, attribute inference attacks [11, 17, 28], website fingerprinting attacks [7, 22, 29, 46, 67], side-channel attacks [73], location attacks [5, 45, 52, 72], and author identification attacks [8, 41].”
Website Fingerprinting at Internet Scale (2016, methodology): “[21] increased the recognition rate for Tor to an alarming degree using an approach based on Support Vector Machines (SVM) in 2011: in the dataset provided by Herrman et al.”
Dissent in Numbers: Making Strong Anonymity Scale (2012, unknown): “analysis attacks [6,13,37,45] further accentuate this class of vulnerabilities.”
Effective Attacks and Provable Defenses for Website Fingerprinting (2014, methodology): “[17] showed an attack that succeeded against web-browsing clients that use Tor.”