Inferring the source of encrypted HTTP connections

By M. Liberatore, Brian Neil Levine. Conference on Computer and Communications Security, 2006

449 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/7bd8ef5acedca33baac97d2cebc7c942a68fb29e

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In Artificial Intelligence

In Computer Networks and Communications

In Information Systems

Citations in the corpus

FlowPrint: Semi-Supervised Mobile-App Fingerprinting on Encrypted Network Traffic (2020, methodology): “Other approaches include the use of Naı̈ve Bayes classifiers in combination with incoming and outgoing byte distributions [39], the use of statistical flow features in combination with decision trees [12] and the possibility of combining existing classifiers [2].”
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.”
Traffic Morphing: An Efficient Defense Against Statistical Traffic Analysis (2009, methodology): “Cretu et al. [7] then presented techniques for cleansing the training data to remove anomalous or adversarially-inserted data before applying learning techniques for signature generation.”
A Critical Evaluation of Website Fingerprinting Attacks (2014, background): “Many WF studies on one-hop proxy attacks have followed [2,9, 16,18].”
A Systematic Approach to Developing and Evaluating Website Fingerprinting Defenses (2014, background): “This is a modiﬁed version of the early Jaccard co-efﬁcient classiﬁer introduced in [9].”
AppScanner: Automatic Fingerprinting of Smartphone Apps from Encrypted Network Traffic (2016, methodology): “Liberatore et al. [11] showed the effectiveness of proposals aiming to identify web-pages via encrypted HTTP trafﬁc analysis.”
Effective Attacks and Provable Defenses for Website Fingerprinting (2014, methodology): “This is similar to the algorithms used by Liberatore and Levine [14] and Herrmann et al. [10], where the presence of unique packet lengths is an important feature.”
Automated Website Fingerprinting through Deep Learning (2017, unknown): “The ﬁrst WF studies evaluated the effectiveness of the attack against HTTPS [9], encrypted web proxies [18], [28], OpenSSH [23] and VPNs [16] and it was not until 2009 that the ﬁrst evaluation of a WF attack was performed in Tor [16].”
k-fingerprinting: A Robust Scalable Website Fingerprinting Technique (2015, background): “They were able to show that this defense does well at defeating early website fingerprinting attacks that heavily relied on exploiting unique packet length features [21, 32].”
Mobile Encrypted Traffic Classification Using Deep Learning: Experimental Evaluation, Lessons Learned, and Challenges (2019, methodology): “[31] and [32] are also employed by Alan and Kaur [30] to check out whether Android apps can be identified from their launch-time traffic using only TCP/IP header information.”
Website fingerprinting in onion routing based anonymization networks (2011, background): “, [11, 14], only used the packet size and whether packets are incoming or outgoing as features.”
Peek-a-boo: i see your smart home activities, even encrypted! (2018, unknown): “For example, web page identiﬁcation [34], web user identiﬁcation [24], protocol identiﬁcation [39] are some of the research on the identiﬁcation using the encrypted trafﬁc.”
Robust Smartphone App Identification via Encrypted Network Traffic Analysis (2017, methodology): “[13] outperformed the aforementioned work by Liberatore and Levine [12] by presenting a method that relies on common text mining techniques performed over the normalized frequency distribution of observable IP packet sizes.”
Touching from a distance: website fingerprinting attacks and defenses (2012, background): “Previous research has shown that web page fingerprinting attacks are possible against many privacy services, including IPSec tunnels, SSH tunnels, and Tor [21, 10, 17, 6, 13].”
Improved website fingerprinting on Tor (2013, methodology): “Tor is a popular anonymity network, currently used by around 500,000 daily clients and carrying 2500 MB of data per second [15].”
No NAT'd User Left Behind: Fingerprinting Users behind NAT from NetFlow Records Alone (2014, background): “Their basic [34], [35], [32], [4], [16], [24] [36], [12] Trafﬁc and Application Proﬁling [3], [25], [28] [37], [23], [15], [29] [6] Information Leaks [19], [30], [5], [33] Device Proﬁling [17], [11], [8] technique though cannot be used to re-identify users later on.”
Website Fingerprinting at Internet Scale (2016, methodology): “Padding as a basic countermeasure was first studied by Liberatore and Levine [16].”