The increasing prevalence of encrypted traffic in

modern networks poses significant challenges for network security,

particularly in detecting and classifying malicious activities

and application signatures. To overcome this issue, deep learning

has turned out to be a promising candidate owing to its ability

to learn complex data patterns. In this work, we present a

deep learning-based novel and robust framework for encrypted

traffic analysis (ETA) which leverages the power of Bidirectional

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[1] Aamina Hassan, "CSTNET", IEEE Dataport, 2024. [Online]. Available: http://dx.doi.org/10.21227/4394-fv34. Accessed: Dec. 26, 2024.
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doi = {10.21227/4394-fv34},
url = {http://dx.doi.org/10.21227/4394-fv34},
author = {Aamina Hassan },
publisher = {IEEE Dataport},
title = {CSTNET},
year = {2024} }
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AU - Aamina Hassan
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Aamina Hassan. (2024). CSTNET. IEEE Dataport. http://dx.doi.org/10.21227/4394-fv34
Aamina Hassan, 2024. CSTNET. Available at: http://dx.doi.org/10.21227/4394-fv34.
Aamina Hassan. (2024). "CSTNET." Web.
1. Aamina Hassan. CSTNET [Internet]. IEEE Dataport; 2024. Available from : http://dx.doi.org/10.21227/4394-fv34
Aamina Hassan. "CSTNET." doi: 10.21227/4394-fv34