Peek-a-Boo, I Still See You: Why Efficient Traffic Analysis Countermeasures Fail

By Kevin P. Dyer, Scott E. Coull, Thomas Ristenpart, Thomas Shrimpton. IEEE Symposium on Security and Privacy, 2012

528 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/af7887649aaec82c123ea6d140a04e2209fdcf73

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Citations in the corpus

No NAT'd User Left Behind: Fingerprinting Users behind NAT from NetFlow Records Alone (2014, background): “Indeed, studies like [10] have proven that by using only coarse features such as total upstream and downstream bandwidth, it is possible to infer private and sensitive information from the encrypted network traffic.”
: Privacy on Mobile Devices – It ’ s Complicated (2016, background): “, decrypting voice over IP (VoIP) [146–148], website fingerprinting [149]).”
A Critical Evaluation of Website Fingerprinting Attacks (2014, methodology): “Total time D [9] N-grams Up/Downstream bytes Bytes in traffic bursts”
Deep Fingerprinting: Undermining Website Fingerprinting Defenses with Deep Learning (2018, methodology): “However, coarse features such as total volume, size and time were hard to conceal without incurring high bandwidth overheads [12].”
Touching from a distance: website fingerprinting attacks and defenses (2012, methodology): “Dyer, et al. showed that all these schemes are broken [6].”
SoK: Privacy on Mobile Devices – It’s Complicated (2016, background): “, decrypting voice over IP (VoIP) [146–148], website fingerprinting [149]).”
Robust Smartphone App Identification via Encrypted Network Traffic Analysis (2017, methodology): “[16] presented web-page fingerprinting attacks and discussed their effectiveness despite traffic analysis countermeasures (e.”
Peek-a-boo: i see your smart home activities, even encrypted! (2018, methodology): “ocol identification [49] are some of the research on the identification using the encrypted traffic. Not only identification attacks, but also the countermeasures have been studied in several studies [10, 19]. FingerprintingMethods.Inalltheaforementionedstudies,either statistical techniques [48] or ”
Spying on the Smart Home: Privacy Attacks and Defenses on Encrypted IoT Traffic (2017, background): “It is generally believed that independent link padding algorithms require too much latency or bandwidth overhead to be feasible in practice [16].”
Website Fingerprinting at Internet Scale (2016, methodology): “[9] showed traffic morphing to be ineffective as a defense against WFP in practice.”
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.”
Improved website fingerprinting on Tor (2013, background): “The probability of an observed traffic instance belonging to a given class (representing one site) is computed from the product of the probabilities of the observed packet lengths occurring at their observed frequencies.”