Paper Citation Record · LEDGER

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers

As of 21 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2506.10851.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.10851 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:21:58.514921Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

31 of 31 outbound references displayed

verified exact1
verified fuzzy24
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4019a303-0b89-4c67-8b9e-80a3bd893c95 · outbound

This paper cites A novel traffic classification ap- proach by employing deep learning on software-defined networking,.

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers A novel traffic classification ap- proach by employing deep learning on software-defined networking,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:22:02.601839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 84722f3b-712d-4fd5-985d-0ffe64614670 · outbound

This paper cites Glads: A global-local attention data selection model for multimodal multitask encrypted traffic classification of iot,.

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers Glads: A global-local attention data selection model for multimodal multitask encrypted traffic classification of iot,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:22:02.465498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e29cba0e-b037-4922-82dd-5b9c8a62ed2f · outbound

This paper cites Cbs: A deep learning approach for encrypted traffic classification with mixed spatio- temporal and statistical features,.

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers Cbs: A deep learning approach for encrypted traffic classification with mixed spatio- temporal and statistical features,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:22:02.291876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9226f3e1-ead6-4afe-af06-e15592c6119f · outbound

This paper cites Deep learning and pre- training technology for encrypted traffic classification: A comprehensive review,.

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers Deep learning and pre- training technology for encrypted traffic classification: A comprehensive review,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:22:02.071724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f68fd35e-0c38-4c0a-98b8-b7c198473fc5 · outbound

This paper cites Advancing network security in industrial iot: A deep dive into ai-enabled intrusion detection systems,.

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers Advancing network security in industrial iot: A deep dive into ai-enabled intrusion detection systems,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:22:01.903457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 66a52c43-8340-4aad-90a3-62926d311643 · outbound

This paper cites Machine learning for encrypted malicious traffic detection: Approaches, datasets and comparative study,.

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers Machine learning for encrypted malicious traffic detection: Approaches, datasets and comparative study,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:22:01.798731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4a1ee625-7b36-4f73-a6ad-240a7cd36d8b · outbound

This paper cites A novel and effective encrypted traffic classification method based on channel attention and deformable convolution,.

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers A novel and effective encrypted traffic classification method based on channel attention and deformable convolution,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:22:01.623918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 10b27dbf-b94a-40fa-87af-fad55bbcdc0c · outbound

This paper cites End-to-end encrypted traffic classification with one-dimensional convolution neural networks,.

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers End-to-end encrypted traffic classification with one-dimensional convolution neural networks,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:22:01.456111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f2bf6230-cc64-441e-820e-4d0e57228a5b · outbound

This paper cites Deep packet: A novel approach for encrypted traffic classification using deep learning,.

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers Deep packet: A novel approach for encrypted traffic classification using deep learning,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:22:01.328052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 219dd8b3-06e0-4567-a42c-6f1217f7e20f · outbound

This paper cites Neural Architecture Search: Insights from 1000 Papers.

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers Neural Architecture Search: Insights from 1000 Papers

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T04:21:57.248302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 455f6c15-ca21-4210-8bd9-95dcf8cd6c97 · outbound

This paper cites Neural architecture search benchmarks: Insights and survey,.

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers Neural architecture search benchmarks: Insights and survey,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:22:01.203542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 86c4fdda-7006-4ba4-b0bd-8016df9682fa · outbound

This paper cites Characterization of encrypted and vpn traffic using time-related,.

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers Characterization of encrypted and vpn traffic using time-related,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T04:21:57.282683Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ed650bf-0a5d-4b1c-884b-d16f11d03fdb · outbound

This paper cites (2022) Hongke sharing — what is deep packet inspection (dpi)? (chinese).

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers (2022) Hongke sharing — what is deep packet inspection (dpi)? (chinese)

Reference 13

Resolution: verified exact; raw_fallback, observed 2026-08-07T04:21:58.814328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 321cc761-0098-40ae-acc5-ed24c8cf7649 · outbound

This paper cites Retracted: Flow online identification method for the encrypted skype,.

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers Retracted: Flow online identification method for the encrypted skype,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:22:01.070753Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 14511afb-bf4f-430c-ab74-019ff4c68b43 · outbound

This paper cites Random forest based traffic classification method in sdn,.

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers Random forest based traffic classification method in sdn,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:22:00.904663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fd1a2d97-e267-429d-8b53-e0505623b13e · outbound

This paper cites Iclstm: encrypted traffic service identification based on inception-lstm neural network,.

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers Iclstm: encrypted traffic service identification based on inception-lstm neural network,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:22:00.754576Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f89c62b9-4bd6-4143-8527-8f962ecac671 · outbound

This paper cites A session- packets-based encrypted traffic classification using capsule neural net- works,.

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers A session- packets-based encrypted traffic classification using capsule neural net- works,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:22:00.611102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1bc76577-be79-457e-ba88-d66fdd960f0c · outbound

This paper cites Network traffic classification model based on attention mechanism and spatiotemporal features,.

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers Network traffic classification model based on attention mechanism and spatiotemporal features,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:22:00.450838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cd6d1503-73ff-4e57-9937-7af17fffee2e · outbound

This paper cites Neural Architecture Search with Reinforcement Learning.

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers Neural Architecture Search with Reinforcement Learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T04:21:57.611326Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dfaba9a7-2178-4409-a995-310a079a61f8 · outbound

This paper cites DARTS: Differentiable Architecture Search.

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers DARTS: Differentiable Architecture Search

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T04:21:57.683045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4db01de-5b37-44b8-a779-c7acd1501e44 · outbound

This paper cites A Comprehensive Survey on Hardware-Aware Neural Architecture Search.

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers A Comprehensive Survey on Hardware-Aware Neural Architecture Search

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T04:21:57.771370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 696b6999-d746-42c4-bae7-11c57d260b41 · outbound

This paper cites An afford- able hardware-aware neural architecture search for deploying convolu- tional neural networks on ultra-low-power computing platforms,.

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers An afford- able hardware-aware neural architecture search for deploying convolu- tional neural networks on ultra-low-power computing platforms,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:22:00.297432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cf4dac5d-fdf0-46c1-8100-df4866c2d24c · outbound

This paper cites Multi-objective hardware-aware neural architecture search using hardware cost diversity,.

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers Multi-objective hardware-aware neural architecture search using hardware cost diversity,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:22:00.160142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 18d2610d-f053-4e6e-8891-7f97cd351aeb · outbound

This paper cites Combining com- pressed sensing and neural architecture search for sensor-near vibration diagnostics,.

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers Combining com- pressed sensing and neural architecture search for sensor-near vibration diagnostics,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:21:59.987879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f335bff0-f96d-4907-81ab-64a1a011e3e9 · outbound

This paper cites Compression- accuracy co-optimization through hardware-aware neural architecture search for vibration damage detection,.

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers Compression- accuracy co-optimization through hardware-aware neural architecture search for vibration damage detection,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:21:59.843610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 7eac9f17-7d07-48f6-93e7-127f12b3a14e · outbound

This paper cites Tiny neural net- works for session-level traffic classification,.

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers Tiny neural net- works for session-level traffic classification,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:21:59.699011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4a87ffce-e5b7-4f6a-a062-f3cad0741651 · outbound

This paper cites Malware traffic classification using convolutional neural network for representation learning,.

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers Malware traffic classification using convolutional neural network for representation learning,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T04:21:58.203107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee3113e5-c3b2-400f-bc35-527471438958 · outbound

This paper cites Centime: A direct comprehensive traffic features extraction for en- crypted traffic classification,.

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers Centime: A direct comprehensive traffic features extraction for en- crypted traffic classification,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:21:59.553183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 12fdb5d2-e884-4773-9fcb-5d20de4b3b1e · outbound

This paper cites Identification of encrypted traffic through attention mechanism based long short term memory,.

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers Identification of encrypted traffic through attention mechanism based long short term memory,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:21:59.332896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 54877200-4370-483d-9111-05c57cca219d · outbound

This paper cites Encrypted traffic classification based on text convolution neural networks,.

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers Encrypted traffic classification based on text convolution neural networks,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:21:59.155007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 360de41d-ec18-48db-a211-c7318bdd8f5f · outbound

This paper cites An encrypted traffic classification framework based on convolutional neural networks and stacked autoencoders,.

Energy-Efficient Deep Learning for Traffic Classification on Microcontrollers An encrypted traffic classification framework based on convolutional neural networks and stacked autoencoders,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:21:58.967335Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

No inbound Pith citation observations are available.