Balancing Complexity and Performance in Convolutional Neural Network Models for QUIC Traffic Classification
The upcoming deployment of sixth-generation (6G) wireless networks promises to significantly outperform 5G in terms of data rates, spectral efficiency, device densities, and, most importantly, latency and security. To cope with the increasingly complex network traffic, Network Traffic Classification...
Saved in:
| Main Authors: | Giovanni Pettorru, Matteo Flumini, Marco Martalò |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-07-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/15/4576 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
RT-QuIC optimization for prion detection in soils
by: Madeline K. Grunklee, et al.
Published: (2025-06-01) -
DDoS classification of network traffic in software defined networking SDN using a hybrid convolutional and gated recurrent neural network
by: Ahmed M. Elshewey, et al.
Published: (2025-08-01) -
MeshHSTGT: hierarchical spatio-temporal fusion for mesh network traffic forecasting
by: Sunlei Qian, et al.
Published: (2025-07-01) -
quicR: An R library for streamlined data handling of real-time quaking induced conversion assays
by: Gage R. Rowden, et al.
Published: (2025-09-01) -
Improved leukocyte classification in bone marrow cytology using convolutional neural network with contrast enhancement
by: Shahid Mehmood, et al.
Published: (2025-08-01)