Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification-Reference-Cited by-同舟云学术

Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification

Published:2024-01 Issue:1 Volume:5 Page:420-433
ISSN:2691-4581
Container-title:IEEE Transactions on Artificial Intelligence
language:
Short-container-title:IEEE Trans. Artif. Intell.

Author:

Jorgensen Steven¹^ORCID,Holodnak John¹^ORCID,Dempsey Jensen¹^ORCID,de Souza Karla¹^ORCID,Raghunath Ananditha¹,Rivet Vernon¹^ORCID,DeMoes Noah¹,Alejos Andrés¹,Wollaber Allan¹^ORCID

Affiliation:

1. MIT Lincoln Laboratory, Lexington, MA, USA

Funder

U.S. Cyber Command under Air Force

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Artificial Intelligence

Link

http://xplorestaging.ieee.org/ielx7/9078688/10384607/10044382.pdf?arnumber=10044382

Reference82 articles.

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3. WatchGuard threat lab reports 91.5 of malware arrived over encrypted connections in Q2 2021;Warfield,2021

4. Automated Website Fingerprinting through Deep Learning

5. Domain name encryption is not enough: privacy leakage via IP-based website fingerprinting

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