Paper Citation Record · LEDGER

On Defending Against Label Flipping Attacks on Malware Detection Systems

As of 16 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:1908.04473.

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

pith.paper-citation-record.v1
1908.04473 v3

Coverage vector

measured 39 of 39 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T13:45:42.940263Z

measured 39 of 39 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

39 of 39 outbound references displayed

verified exact0
verified fuzzy28
unresolved6
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation ba006bb6-0900-4f18-8f31-f5ea950e8951 · outbound

This paper cites http://contagiominidump.blogspot.

On Defending Against Label Flipping Attacks on Malware Detection Systems http://contagiominidump.blogspot

Reference 1

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Source-reported events for the cited work

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Observation c85cfacb-467c-4d01-aa24-fb2d8bcfdefe · outbound

This paper cites https://scikit- learn.org/stable/ modules/label propagation.html (2020).

On Defending Against Label Flipping Attacks on Malware Detection Systems https://scikit- learn.org/stable/ modules/label propagation.html (2020)

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:45:43.459113Z

Source-reported events for the cited work

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Observation e4253733-4d7b-4428-bfb4-d9d060f0069c · outbound

This paper cites https://nlp.stanford.edu/IR-book/ html/htmledition/mutual- information- 1.html (2020).

On Defending Against Label Flipping Attacks on Malware Detection Systems https://nlp.stanford.edu/IR-book/ html/htmledition/mutual- information- 1.html (2020)

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:45:43.446456Z

Source-reported events for the cited work

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Observation fc6124d5-e246-4af9-94d1-65d7d7b564f5 · outbound

This paper cites In: Ndss, vol.

On Defending Against Label Flipping Attacks on Malware Detection Systems In: Ndss, vol

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:45:43.433429Z

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Observation 7189ab29-04eb-473d-8ce9-a32f00af6569 · outbound

This paper cites Energy Models for Better Pseudo-Labels: Improving Semi-Supervised Classification with the 1-Laplacian Graph Energy.

On Defending Against Label Flipping Attacks on Malware Detection Systems Energy Models for Better Pseudo-Labels: Improving Semi-Supervised Classification with the 1-Laplacian Graph Energy

Reference 5

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Observation a74b8705-ce65-4654-8a3c-f32b9557f037 · outbound

This paper cites In: 2018 IEEE International Congress on Internet of Things (ICIOT), pp.

On Defending Against Label Flipping Attacks on Malware Detection Systems In: 2018 IEEE International Congress on Internet of Things (ICIOT), pp

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:45:43.420652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7ff0fb8f-b5a5-4a86-8c37-44f48fdcf9b9 · outbound

This paper cites Enhancing Robustness of Machine Learning Systems via Data Transformations.

On Defending Against Label Flipping Attacks on Malware Detection Systems Enhancing Robustness of Machine Learning Systems via Data Transformations

Reference 7

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Source-reported events for the cited work

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Observation 91a70337-3700-434f-a7a5-1c128747d97b · outbound

This paper cites Neuro- computing 192, 61–71 (2016).

On Defending Against Label Flipping Attacks on Malware Detection Systems Neuro- computing 192, 61–71 (2016)

Reference 8

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fd1031a6-5601-460e-b00c-9e6fd0665c71 · outbound

This paper cites In: Joint European con- ference on machine learning and knowledge discovery in databases, pp.

On Defending Against Label Flipping Attacks on Malware Detection Systems In: Joint European con- ference on machine learning and knowledge discovery in databases, pp

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:45:43.393050Z

Source-reported events for the cited work

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Observation 1116695f-8347-4d5a-9180-4abef575170f · outbound

This paper cites Pattern Recognition 47(11), 3641–3655 (2014).

On Defending Against Label Flipping Attacks on Malware Detection Systems Pattern Recognition 47(11), 3641–3655 (2014)

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:45:43.380532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 70bfd95a-9d09-4f95-bc01-20044b54b0f8 · outbound

This paper cites In: Proceedings of Twenty- Seventh International Joint Conference on Artiﬁcial In- telligence, pp.

On Defending Against Label Flipping Attacks on Malware Detection Systems In: Proceedings of Twenty- Seventh International Joint Conference on Artiﬁcial In- telligence, pp

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:45:43.366689Z

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Observation 01cbafc1-6b24-493e-879f-976aa350d9a7 · outbound

This paper cites In: ESANN, pp.

On Defending Against Label Flipping Attacks on Malware Detection Systems In: ESANN, pp

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:45:43.352844Z

Source-reported events for the cited work

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Observation bf115ab3-03f6-4c6e-84c1-ad9071720f51 · outbound

This paper cites The Journal of Machine Learning Research 17(1), 2096–2030 (2016).

On Defending Against Label Flipping Attacks on Malware Detection Systems The Journal of Machine Learning Research 17(1), 2096–2030 (2016)

Reference 13

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Source-reported events for the cited work

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Observation f83b191c-edf4-46f7-a327-4a8cf6c1d9dd · outbound

This paper cites In: 2019 13th European Conference on Antennas and Propagation (EuCAP), pp.

On Defending Against Label Flipping Attacks on Malware Detection Systems In: 2019 13th European Conference on Antennas and Propagation (EuCAP), pp

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:45:43.332430Z

Source-reported events for the cited work

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Observation 936c2795-a15b-4e84-8fd4-3dc970721d20 · outbound

This paper cites In: Proceedings of the IEEE con- ference on computer vision and pattern recognition, pp.

On Defending Against Label Flipping Attacks on Malware Detection Systems In: Proceedings of the IEEE con- ference on computer vision and pattern recognition, pp

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:45:43.319705Z

Source-reported events for the cited work

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Observation d756a383-c6c7-460b-b564-6faaf2df61c5 · outbound

This paper cites IEEE Transactions on Image Processing 14(3), 360–369 (2005).

On Defending Against Label Flipping Attacks on Malware Detection Systems IEEE Transactions on Image Processing 14(3), 360–369 (2005)

Reference 16

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Source-reported events for the cited work

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Observation 6811faf1-5db5-4bf5-9bb3-e884012b1907 · outbound

This paper cites In: Proceed- ings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.

On Defending Against Label Flipping Attacks on Malware Detection Systems In: Proceed- ings of the IEEE Conference on Computer Vision and Pattern Recognition, pp

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:45:43.294006Z

Source-reported events for the cited work

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Observation 1ff80814-379b-47b5-b4a4-f83b40139606 · outbound

This paper cites In: Proc.

On Defending Against Label Flipping Attacks on Malware Detection Systems In: Proc

Reference 18

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Observation dcaf0c18-fab7-4a0c-a4bb-e88d46722010 · outbound

This paper cites Neural GPUs Learn Algorithms.

On Defending Against Label Flipping Attacks on Malware Detection Systems Neural GPUs Learn Algorithms

Reference 19

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Observation bd5ea6b0-2135-44fd-a893-4500ec4a72f6 · outbound

This paper cites Curie: A method for protecting SVM Classifier from Poisoning Attack.

On Defending Against Label Flipping Attacks on Malware Detection Systems Curie: A method for protecting SVM Classifier from Poisoning Attack

Reference 20

Resolution: metadata mismatch; local_arxiv, observed 2026-08-14T13:45:43.054362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2bc52b92-c350-41fa-b06b-5a56f3f6e1aa · outbound

This paper cites IEEE transactions on pattern analysis and machine intelligence 37(1), 175–188 (2014).

On Defending Against Label Flipping Attacks on Malware Detection Systems IEEE transactions on pattern analysis and machine intelligence 37(1), 175–188 (2014)

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:45:43.269130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 99e19cef-a30c-4260-9b3b-79163172e5f2 · outbound

This paper cites In: International Conference on Machine Learning, pp.

On Defending Against Label Flipping Attacks on Malware Detection Systems In: International Conference on Machine Learning, pp

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:45:43.256522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f632802b-40cd-4009-94e8-010c1ffbd83e · outbound

This paper cites In: Proceedings of the 10th ACM Workshop on Artiﬁcial Intelligence and Security, pp.

On Defending Against Label Flipping Attacks on Malware Detection Systems In: Proceedings of the 10th ACM Workshop on Artiﬁcial Intelligence and Security, pp

Reference 23

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Source-reported events for the cited work

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Observation eb3c4922-1ec2-4fbd-b88c-a710b3bef65c · outbound

This paper cites In: Advances in neural information processing systems, pp.

On Defending Against Label Flipping Attacks on Malware Detection Systems In: Advances in neural information processing systems, pp

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:45:43.231833Z

Source-reported events for the cited work

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Observation 09c3b6b2-ee37-458d-b295-fcd0a3971f8a · outbound

This paper cites In: Proc.

On Defending Against Label Flipping Attacks on Malware Detection Systems In: Proc

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:45:43.219844Z

Source-reported events for the cited work

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Observation ddc6dd59-f3fb-4f3d-a8bf-4a90323d0d6a · outbound

This paper cites In: Joint European Conference on Machine Learning and Knowl- edge Discovery in Databases, pp.

On Defending Against Label Flipping Attacks on Malware Detection Systems In: Joint European Conference on Machine Learning and Knowl- edge Discovery in Databases, pp

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:45:43.207592Z

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Observation e17017d5-ab8b-4624-ad80-26288983daa1 · outbound

This paper cites Journal of the American Statistical as- sociation 66(336), 846–850 (1971).

On Defending Against Label Flipping Attacks on Malware Detection Systems Journal of the American Statistical as- sociation 66(336), 846–850 (1971)

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:45:43.195549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3861dc7f-6def-4d84-b51b-553c0c0cf790 · outbound

This paper cites Learning to Reweight Examples for Robust Deep Learning.

On Defending Against Label Flipping Attacks on Malware Detection Systems Learning to Reweight Examples for Robust Deep Learning

Reference 28

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Observation e72af710-92a0-4c75-9984-13105bf5ae9e · outbound

This paper cites In: Advances in Neural Information Processing Systems, pp.

On Defending Against Label Flipping Attacks on Malware Detection Systems In: Advances in Neural Information Processing Systems, pp

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:45:43.183792Z

Source-reported events for the cited work

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Observation bc14f420-8def-422d-bc09-c6c34148af1f · outbound

This paper cites Can Machine Learning Model with Static Features be Fooled: an Adversarial Machine Learning Approach.

On Defending Against Label Flipping Attacks on Malware Detection Systems Can Machine Learning Model with Static Features be Fooled: an Adversarial Machine Learning Approach

Reference 30

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Source-reported events for the cited work

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Observation 2f7eeaaf-0e2b-4cce-90fb-687f36ea384a · outbound

This paper cites https://github.com/ mshojafar / sourcecodes / blob / master / Taheri % 20et % 20al- NCAA2020.zip (2020).

On Defending Against Label Flipping Attacks on Malware Detection Systems https://github.com/ mshojafar / sourcecodes / blob / master / Taheri % 20et % 20al- NCAA2020.zip (2020)

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:45:43.171771Z

Source-reported events for the cited work

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Observation d57cd22f-afd4-4118-af5e-1a0b7f79feb1 · outbound

This paper cites Data Poisoning Attacks against Online Learning.

On Defending Against Label Flipping Attacks on Malware Detection Systems Data Poisoning Attacks against Online Learning

Reference 32

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Source-reported events for the cited work

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Observation 031b891b-c0b8-42a8-8b60-8ccb5c5874e7 · outbound

This paper cites 3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training.

On Defending Against Label Flipping Attacks on Malware Detection Systems 3D Semi-Supervised Learning with Uncertainty-Aware Multi-View Co-Training

Reference 33

Resolution: metadata mismatch; local_arxiv, observed 2026-08-14T13:45:42.993295Z

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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0be10784-93a4-4716-8242-516d90999171 · outbound

This paper cites 1689–1698 (2015).

On Defending Against Label Flipping Attacks on Malware Detection Systems 1689–1698 (2015)

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:45:43.158954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 490505e5-c7e1-4c28-a2a4-d5f658a05e30 · outbound

This paper cites Neurocomputing 160, 53–62 (2015).

On Defending Against Label Flipping Attacks on Malware Detection Systems Neurocomputing 160, 53–62 (2015)

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:45:43.146621Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8ad5478e-a960-4d5c-acd2-ee91b34d711a · outbound

This paper cites Generative Poisoning Attack Method Against Neural Networks.

On Defending Against Label Flipping Attacks on Malware Detection Systems Generative Poisoning Attack Method Against Neural Networks

Reference 36

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8a7b0c93-1436-4126-bd06-a87fd9b61a45 · outbound

This paper cites IEEE transactions on cybernetics 46(3), 766–777 (2016).

On Defending Against Label Flipping Attacks on Malware Detection Systems IEEE transactions on cybernetics 46(3), 766–777 (2016)

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:45:43.134105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2fb1eb06-2d49-425d-8347-8e33065bef53 · outbound

This paper cites In: Pro- ceedings of the 18th ACM SIGKDD international confer- ence on Knowledge discovery and data mining, pp.

On Defending Against Label Flipping Attacks on Malware Detection Systems In: Pro- ceedings of the 18th ACM SIGKDD international confer- ence on Knowledge discovery and data mining, pp

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T13:45:43.121626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d47ed303-5be3-4a97-9506-c735f3d05e10 · outbound

This paper cites SUPERFLUIDITY.

On Defending Against Label Flipping Attacks on Malware Detection Systems SUPERFLUIDITY

Reference 1067

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.