Paper Citation Record · LEDGER

Robust DDoS-Attack Classification with 3D CNNs Against Adversarial Methods

As of 22 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2509.10543.

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

pith.paper-citation-record.v1
2509.10543 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:24:47.775180Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

21 of 21 outbound references displayed

verified exact6
verified fuzzy3
unresolved7
parse uncertain0
malformed identifier5
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d99a0b2d-8222-44f1-a612-3a8d154e19bc · outbound

This paper cites A Novel Framework for Spatio-Temporal Prediction of Environmental Data Using Deep Learning.

Robust DDoS-Attack Classification with 3D CNNs Against Adversarial Methods A Novel Framework for Spatio-Temporal Prediction of Environmental Data Using Deep Learning

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-15T16:24:47.903380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation abc91407-2ac1-43ee-a59c-6b7e29f5e256 · outbound

This paper cites IEEE Transactions on Technology and Society3(3), 155–162 (2022).https://doi.org/10.1109/TTS.

Robust DDoS-Attack Classification with 3D CNNs Against Adversarial Methods IEEE Transactions on Technology and Society3(3), 155–162 (2022).https://doi.org/10.1109/TTS

Reference 2

Resolution: malformed identifier; doi_truncated, observed 2026-08-15T16:24:48.206311Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e1dbe614-45b4-4b75-8939-ddb1108cba5b · outbound

This paper cites 606–617 (2016).https://doi.org/10.1007/ 978-3-319-54660-5_54.

Robust DDoS-Attack Classification with 3D CNNs Against Adversarial Methods 606–617 (2016).https://doi.org/10.1007/ 978-3-319-54660-5_54

Reference 3

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T16:24:48.261468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 35ba8332-90b5-4298-8d85-c173e95a04a5 · outbound

This paper cites 5729–5738 (2017).https://doi.org/10.

Robust DDoS-Attack Classification with 3D CNNs Against Adversarial Methods 5729–5738 (2017).https://doi.org/10

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:24:48.250523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 3e3db04c-0844-4a05-ad35-ba770539d237 · outbound

This paper cites Explaining and Harnessing Adversarial Examples.

Robust DDoS-Attack Classification with 3D CNNs Against Adversarial Methods Explaining and Harnessing Adversarial Examples

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-15T16:24:47.712876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae999c74-011b-45f3-8302-887f7f9f35ae · outbound

This paper cites In: 2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON).

Robust DDoS-Attack Classification with 3D CNNs Against Adversarial Methods In: 2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T16:24:47.717024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51804c4a-1bd6-44b9-a026-3b50d59466d7 · outbound

This paper cites 5308–5317 (2016).https://doi.org/10.1109/cvpr.

Robust DDoS-Attack Classification with 3D CNNs Against Adversarial Methods 5308–5317 (2016).https://doi.org/10.1109/cvpr

Reference 7

Resolution: malformed identifier; no resolver link, observed 2026-08-15T16:24:47.721128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64b5a08b-f070-4fc7-9087-2f3c98e8d5dc · outbound

This paper cites IEEE Transactions on Pattern Analysis and Machine Intelligence 35(1), 221–231 (2013).https://doi.org/10.1109/TPAMI.2012.59.

Robust DDoS-Attack Classification with 3D CNNs Against Adversarial Methods IEEE Transactions on Pattern Analysis and Machine Intelligence 35(1), 221–231 (2013).https://doi.org/10.1109/TPAMI.2012.59

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T16:24:47.724926Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0fdd9e16-35ce-4f09-a18c-f643c6ac9773 · outbound

This paper cites 816–833 (2016).https://doi.org/10.1007/ 978-3-319-46487-9_50.

Robust DDoS-Attack Classification with 3D CNNs Against Adversarial Methods 816–833 (2016).https://doi.org/10.1007/ 978-3-319-46487-9_50

Reference 9

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T16:24:48.240351Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation b3ecdf03-d89f-4db7-9f34-f2e88d0bed43 · outbound

This paper cites Towards Deep Learning Models Resistant to Adversarial Attacks.

Robust DDoS-Attack Classification with 3D CNNs Against Adversarial Methods Towards Deep Learning Models Resistant to Adversarial Attacks

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T16:24:47.731968Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a94aab67-53cf-4517-89d7-b1f338e729f9 · outbound

This paper cites In: 2018 International Interdisciplinary PhD Work- shop (IIPhDW).

Robust DDoS-Attack Classification with 3D CNNs Against Adversarial Methods In: 2018 International Interdisciplinary PhD Work- shop (IIPhDW)

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T16:24:47.735671Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17683dc6-add9-4544-93e0-5f9661a51114 · outbound

This paper cites 1–36 (2005).https://doi.org/10.

Robust DDoS-Attack Classification with 3D CNNs Against Adversarial Methods 1–36 (2005).https://doi.org/10

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:24:48.229873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0f3fbefa-b6c9-4ad9-89b2-729e0ac36532 · outbound

This paper cites an unresolved cited work.

Robust DDoS-Attack Classification with 3D CNNs Against Adversarial Methods Unresolved cited work

Reference 13

Resolution: verified exact; doi, observed 2026-08-15T16:24:47.871580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5f80097a-bfb0-4056-b82b-758d8f643c5a · outbound

This paper cites In: 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communica- tion Conference (UEMCON).

Robust DDoS-Attack Classification with 3D CNNs Against Adversarial Methods In: 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communica- tion Conference (UEMCON)

Reference 14

Resolution: malformed identifier; no resolver link, observed 2026-08-15T16:24:47.747253Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94cde513-7a71-40e3-b777-42793e0e1821 · outbound

This paper cites 4489–4497 (2015).https: //doi.org/10.1109/iccv.2015.510.

Robust DDoS-Attack Classification with 3D CNNs Against Adversarial Methods 4489–4497 (2015).https: //doi.org/10.1109/iccv.2015.510

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T16:24:47.750942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98994e8d-3ebf-4489-92bd-353bfaf4efd5 · outbound

This paper cites In: 2015 IEEE International Con- ference on Computer Vision (ICCV).

Robust DDoS-Attack Classification with 3D CNNs Against Adversarial Methods In: 2015 IEEE International Con- ference on Computer Vision (ICCV)

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:24:48.217668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 142abc56-a31f-40ad-91b9-9d3ba32ba603 · outbound

This paper cites an unresolved cited work.

Robust DDoS-Attack Classification with 3D CNNs Against Adversarial Methods Unresolved cited work

Reference 17

Resolution: verified exact; raw_fallback, observed 2026-08-15T16:24:47.985702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a3f549b7-5cd6-4640-b5d6-854c769fb8e6 · outbound

This paper cites Deep Learning for Spatio-Temporal Data Mining: A Survey.

Robust DDoS-Attack Classification with 3D CNNs Against Adversarial Methods Deep Learning for Spatio-Temporal Data Mining: A Survey

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T16:24:47.762015Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12b7fb10-6e9b-40ad-9cdf-bd63b399bca8 · outbound

This paper cites Video Playback Rate Perception for Self-supervisedSpatio-Temporal Representation Learning.

Robust DDoS-Attack Classification with 3D CNNs Against Adversarial Methods Video Playback Rate Perception for Self-supervisedSpatio-Temporal Representation Learning

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-15T16:24:47.843118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a727c000-7ef3-4cd8-8bc6-a79637ae0a1d · outbound

This paper cites Shifted Chunk Transformer for Spatio-Temporal Representational Learning.

Robust DDoS-Attack Classification with 3D CNNs Against Adversarial Methods Shifted Chunk Transformer for Spatio-Temporal Representational Learning

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-15T16:24:47.828030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 48dfc2aa-292c-4dca-8beb-7d36351f0464 · outbound

This paper cites Measuring disentangled generative spatio-temporal representation.

Robust DDoS-Attack Classification with 3D CNNs Against Adversarial Methods Measuring disentangled generative spatio-temporal representation

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-15T16:24:47.811544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

No inbound Pith citation observations are available.