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Keyword

encrypted traffic

Description: Network communications whose contents are cryptographically protected, studied in security, traffic management, and application identification.
OpenAlex ID: keywords/encrypted-traffic
Website Fingerprinting at Internet Scale
2016 · Andriy Panchenko, Fabian Lanze, et al. · Network and Distributed System Security Symposium · 645
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Mobile Encrypted Traffic Classification Using Deep Learning: Experimental Evaluation, Lessons Learned, and Challenges
2019 · Giuseppe Aceto, Domenico Ciuonzo, et al. · IEEE Transactions on Network and Service Management · 524
Automated traffic classification and application identification using machine learning
2005 · S. Zander, T. Nguyen, et al. · 517
AppScanner: Automatic Fingerprinting of Smartphone Apps from Encrypted Network Traffic
2016 · Vincent F. Taylor, Riccardo Spolaor, et al. · IEEE European Symposium on Security and Privacy · 421
Detecting stepping stones
2000 · Yin Zhang⋆, Vern Paxson · 390
A survey of methods for encrypted traffic classification and analysis
2015 · Petr Velan, Milan Čermák, et al. · International Journal of Network Management · 380
ACAS
2005 · Patrick Haffner, Subhabrata Sen, et al. · 375
Towards the Deployment of Machine Learning Solutions in Network Traffic Classification: A Systematic Survey
2018 · Fannia Pacheco, Ernesto Exposito, et al. · IEEE Communications Surveys & Tutorials · 369
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Botnet detection based on traffic behavior analysis and flow intervals
2013 · David Zhao, Issa Traore, et al. · Computers & Security · 344
FlowPrint: Semi-Supervised Mobile-App Fingerprinting on Encrypted Network Traffic
2020 · Thijs van Ede, Riccardo Bortolameotti, et al. · Network and Distributed System Security Symposium · 338
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BlindBox
2015 · Justine Sherry, Chang Lan, et al. · 292
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A Survey on Encrypted Network Traffic Analysis Applications, Techniques, and Countermeasures
2021 · Eva Papadogiannaki, Sotiris Ioannidis · ACM Computing Surveys · 286
k-fingerprinting: a Robust Scalable Website Fingerprinting Technique
2015 · Hayes, Jamie, Danezis, George · arXiv (Cornell University) · 279
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A Smart Home is No Castle: Privacy Vulnerabilities of Encrypted IoT Traffic
2017 · Apthorpe, Noah, Reisman, Dillon, et al. · arXiv (Cornell University) · 247
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An SVM-based machine learning method for accurate internet traffic classification
2008 · Ruixi Yuan, Zhu Li, et al. · Information Systems Frontiers · 230
A new network forensic framework based on deep learning for Internet of Things networks: A particle deep framework
2020 · Nickolaos Koroniotis, Nour Moustafa, et al. · Future Generation Computer Systems · 224
On Inferring Application Protocol Behaviors in Encrypted Network Traffic
2006 · Charles V. Wright, Fabian Monrose, et al. · 221
Cookies That Give You Away
2015 · Steven Englehardt, Dillon Reisman, et al. · International World Wide Web Conference (WWW) · 196
Network monitoring using traffic dispersion graphs (tdgs)
2007 · Marios Iliofotou, Prashanth Pappu, et al. · ACM SIGCOMM Internet Measurement Conference (IMC) · 190
Spying on the Smart Home: Privacy Attacks and Defenses on Encrypted IoT Traffic
2017 · Apthorpe, Noah, Reisman, Dillon, et al. · arXiv (Cornell University) · 185
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Measuring Video QoE from Encrypted Traffic
2016 · Giorgos Dimopoulos, Ilias Leontiadis, et al. · Internet Measurement Conference · 176
Analysis of high volumes of network traffic for Advanced Persistent Threat detection
2016 · Mirco Marchetti, Fabio Pierazzi, et al. · Computer Networks · 169
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Machine learning for encrypted malicious traffic detection: Approaches, datasets and comparative study
2021 · Zihao Wang, Kar Wai Fok, et al. · Computers & Security · 162
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Mobile Encrypted Traffic Classification Using Deep Learning
2018 · Giuseppe Aceto, Domenico Ciuonzo, et al. · Network Traffic Measurement and Analysis Conference (TMA) · 155
On using eXtreme Gradient Boosting (XGBoost) Machine Learning algorithm for Home Network Traffic Classification
2019 · Iyad Lahsen Cherif, Abdesselem Kortebi · Wireless Days (WD) · 138
Works count: 2,065
Citation count: 20,520
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