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References from LiteNet: lightweight network traffic classification using explainable artificial intelligence and model compression. Local targets link to admitted publications; unresolved targets remain external evidence.
MalayaNetwork_GT: non-IID dataset for FL-based NTC evaluation
2025 · External reference
Leveraging federated learning and XAI for privacy-aware and lightweight edge training in network traffic classification
2023 · External reference
Performance evaluation metrics for cloud, fog and edge computing: a review, taxonomy, benchmarks and standards for future research
10.1016/j.iot.2020.100273 · 2020 · External reference
Serverless edge computing: vision and challenges
2021 · External reference
FlowLens: enabling efficient flow classification for ML-based network security applications
10.14722/ndss.2021.24067 · 2021 · External reference
GateKeeper
2024 · External reference
L-ETC: a lightweight model based on key bytes selection for encrypted traffic classification
2023 · External reference
GateKeeper: an UltraLite malicious traffic identification method with dual-aspect optimization strategies on IoT gateways
10.1016/j.comnet.2024.110556 · 2024 · External reference
A survey on deep neural network pruning: taxonomy, comparison, analysis, and recommendations
10.1109/tpami.2024.3447085 · 2024 · External reference
Real-time encrypted traffic classification via lightweight neural networks
10.1109/globecom42002.2020.9322309 · 2020 · External reference
MATEC: a lightweight neural network for online encrypted traffic classification
10.1016/j.comnet.2021.108472 · 2021 · External reference
CMFTC: cross modality fusion efficient multitask encrypt traffic classification in IIoT environment
10.1109/tnse.2023.3279427 · 2023 · External reference
Characterization of encrypted and VPN traffic using time-related features
2016 · External reference
VPN-nonVPN dataset (ISCXVPN2016)
2016 · External reference
A lightweight, efficient and explainable-by-design convolutional neural network for internet traffic classification
2023 · External reference
Explainable deep-learning approaches for packet-level traffic prediction of collaboration and communication mobile apps
10.1109/ojcoms.2024.3366849 · 2024 · External reference
LiteNet: lightweight network traffic classification for edge devices
2025 · External reference
Lightweight deep learning-based network traffic classification using explainable artificial intelligence
2024 · External reference
New directions in automated traffic analysis
10.1145/3460120.3484758 · 2021 · External reference
Searching for MobileNetV3
10.1109/iccv.2019.00140 · 2019 · External reference
Facts and figures 2023
2023 · External reference
A comprehensive study on quantization techniques for large language models
10.1109/icairc64177.2024.10899941 · 2024 · External reference
Adaptive and lightweight network traffic classification for edge devices
10.1109/tgcn.2022.3165221 · 2022 · External reference
ET-BERT: a contextualized datagram representation with pre-training transformers for encrypted traffic classification
10.1145/3485447.3512217 · 2022 · External reference
Deep packet: a novel approach for encrypted traffic classification using deep learning
10.1007/s00500-019-04030-2 · 2019 · External reference
Lightweight models for traffic classification: a two-step distillation approach
2022 · External reference
A unified approach to interpreting model predictions
2017 · External reference
Accelerating sparse deep neural networks
10.48550/arxiv.2104.08378 · 2021 · External reference
Improving performance, reliability, and feasibility in multimodal multitask traffic classification with XAI
10.1109/tnsm.2023.3246794 · 2023 · External reference
Realguard: a lightweight network intrusion detection system for IoT gateways
10.3390/s22020432 · 2022 · External reference
A feasible and explainable network traffic classifier utilizing DistilBERT
10.1109/access.2023.3293105 · 2023 · External reference
Learning important features through propagating activation differences
2017 · External reference
Artificial intelligence in the IoT era: a review of edge AI hardware and software
2022 · External reference
Going deeper with convolutions
10.1109/cvpr.2015.7298594 · 2015 · External reference
Explainability methods for identifying root-cause of SLA violation prediction in 5G network
10.1109/globecom42002.2020.9322496 · 2020 · External reference
YOLOv10: real-time end-to-end object detection
10.52202/079017-3429 · 2024 · External reference
Datanet: deep learning based encrypted network traffic classification in SDN home gateway
10.1109/access.2018.2872430 · 2018 · External reference
Pruned-F1DCN: a lightweight network model for traffic classification
2022 · External reference
ABL-TC: a lightweight design for network traffic classification empowered by deep learning
10.1016/j.neucom.2022.03.007 · 2022 · External reference
FastTraffic: a lightweight method for encrypted traffic fast classification
10.1016/j.comnet.2023.109965 · 2023 · External reference
A model of encrypted network traffic classification that trades off accuracy and efficiency
10.1007/s10922-024-09892-y · 2024 · External reference
