A hardware-aware searched 1D-CNN classifies encrypted traffic on STM32 microcontrollers with 96.59% accuracy, 10.08M FLOPs, and 7.86-29.10 mJ per inference.
Cbs: A deep learning approach for encrypted traffic classification with mixed spatio- temporal and statistical features,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.NI 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers
A hardware-aware searched 1D-CNN classifies encrypted traffic on STM32 microcontrollers with 96.59% accuracy, 10.08M FLOPs, and 7.86-29.10 mJ per inference.