Robust open-set classification for encrypted traffic fingerprinting

Author:

Dahanayaka ThiliniORCID,Ginige YasodORCID,Huang YiORCID,Jourjon Guillaume,Seneviratne SurangaORCID

Funder

DSTG

Publisher

Elsevier BV

Subject

Computer Networks and Communications

Reference59 articles.

1. Side-channel leaks in web applications: A reality today, a challenge tomorrow;Chen,2010

2. P. Sirinam, M. Imani, M. Juarez, M. Wright, Deep fingerprinting: Undermining website fingerprinting defenses with deep learning, in: Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security, 2018, pp. 1928–1943.

3. Automated website fingerprinting through deep learning;Rimmer,2018

4. Deep content: Unveiling video streaming content from encrypted wifi traffic;Li,2018

5. SETA: Scalable encrypted traffic analytics in multi-Gbps networks;Choi,2020

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1. Deep learning and pre-training technology for encrypted traffic classification: A comprehensive review;Neurocomputing;2024-08

2. Exploring the Capabilities and Limitations of Video Stream Fingerprinting;2024 IEEE Security and Privacy Workshops (SPW);2024-05-23

3. A comprehensive analysis of website fingerprinting defenses on Tor;Computers & Security;2024-01

4. DataZoo: Streamlining Traffic Classification Experiments;Proceedings of the 2023 on Explainable and Safety Bounded, Fidelitous, Machine Learning for Networking;2023-12-05

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