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Paper Citation Record · LEDGER

Dos and Don'ts of Machine Learning in Computer Security

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2010.09470.

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

pith.paper-citation-record.v1
2010.09470 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T01:09:06.217807Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

24
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 1df4be8d-6549-4c53-9b89-da001751d9f9 · inbound

Contextualizing Security and Privacy of Software-Defined Vehicles: A Literature Review and Industry Perspectives cites this paper.

Contextualizing Security and Privacy of Software-Defined Vehicles: A Literature Review and Industry Perspectives Dos and Don'ts of Machine Learning in Computer Security

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-23T16:53:12.787552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-23T16:52:23.681846Z digest=sha256:0a07d516426d1c82d727e20ccd89aba593d6a82f9dcb2cdfbf4a8952f52fcf67

Observation c388ab16-d55f-458b-8b17-e65a0eeba30b · inbound

Today's Cat Is Tomorrow's Dog: Accounting for Time-Based Changes in the Labels of ML Vulnerability Detection Approaches cites this paper.

Today's Cat Is Tomorrow's Dog: Accounting for Time-Based Changes in the Labels of ML Vulnerability Detection Approaches Dos and Don'ts of Machine Learning in Computer Security

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-07T01:09:06.217807Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T01:09:06.217807Z digest=sha256:17c485817d978f76dc48ba660113800ba8bbdb1f4f60aecb2bdef1f49c9ce93c

Observation cac53a68-30ee-469f-9459-4cc347a44f70 · inbound

PaTAS: A Framework for Trust Propagation in Neural Networks Using Subjective Logic cites this paper.

PaTAS: A Framework for Trust Propagation in Neural Networks Using Subjective Logic Dos and Don'ts of Machine Learning in Computer Security

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-03T20:19:43.937309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T20:19:43.937309Z digest=sha256:333332c3e541f1baa5ed79b9445ebe9fc7cd372c661222293b725f8e9bfbf3fb

Observation d70108aa-0d32-4cc1-af37-0acce02ce5f5 · inbound

Can LLMs Infer Conversational Agent Users' Personality Traits from Chat History? cites this paper.

Can LLMs Infer Conversational Agent Users' Personality Traits from Chat History? Dos and Don'ts of Machine Learning in Computer Security

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-13T23:13:24.711707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-13T23:09:27.704515Z digest=sha256:b2eed4afa54c6fbd7364d21953d01308a7475dfdff06d83fbe885551e4570e98

Observation 96dafe39-1c50-428c-a386-407d8e9803c2 · inbound

Unmasking LAION-5B: Age, Gender, Race, and Emotion Biases in Large-Scale Image Datasets cites this paper.

Unmasking LAION-5B: Age, Gender, Race, and Emotion Biases in Large-Scale Image Datasets Dos and Don'ts of Machine Learning in Computer Security

Reference 14

Resolution
verified exact
arxiv_id, observed 2026-07-04T09:59:45.880494Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-26T09:12:19.873337Z digest=sha256:294a73cf6e82fc500ea9323972cf844ac5240bcc52cc44eb2554ba06a079795e