Paper Citation Record · LEDGER

Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection

As of 10 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2608.03642.

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

pith.paper-citation-record.v1
2608.03642 v2

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T04:30:48.468417Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

19 of 19 outbound references displayed

verified exact0
verified fuzzy1
unresolved18
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5fb3c3ea-f1a6-485a-9d0e-3e50437c2a62 · outbound

This paper cites Androzoo: Collecting millions of android apps for the research community,.

Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection Androzoo: Collecting millions of android apps for the research community,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-10T04:30:48.407682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4afe40a6-22d0-43f6-9848-7dae77ed911b · outbound

This paper cites EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models.

Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T04:30:48.411551Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4e8bf34-08b4-46c8-8c68-b5a7d034f205 · outbound

This paper cites Drebin: Effective and explainable detection of android malware in your pocket.

Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection Drebin: Effective and explainable detection of android malware in your pocket

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T04:30:48.415437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a79f565f-6e12-4894-aa5b-f8058d4f5cf0 · outbound

This paper cites Transcending transcend: Revisiting malware classification in the presence of concept drift,.

Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection Transcending transcend: Revisiting malware classification in the presence of concept drift,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T04:30:48.418531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d5e4e80-5e84-4f2d-996b-7dcf0fced74e · outbound

This paper cites Evading Malware Classifiers via Monte Carlo Mutant Feature Discovery.

Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection Evading Malware Classifiers via Monte Carlo Mutant Feature Discovery

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T04:30:48.422151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e187ae5a-112a-4be9-ab93-e9fb2dc778c1 · outbound

This paper cites Continuous learning for android malware detection,.

Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection Continuous learning for android malware detection,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T04:30:48.425686Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e751dcd-eec7-441c-9430-56d53c28e0c5 · outbound

This paper cites Adversarial exem- ples: Functionality-preserving optimization of adversarial windows malware,.

Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection Adversarial exem- ples: Functionality-preserving optimization of adversarial windows malware,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T04:30:48.429084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 185da30d-342b-480c-ac49-1b116852520b · outbound

This paper cites Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries.

Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T04:30:48.432278Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cec05756-88d5-4e02-8507-a8c8165b2310 · outbound

This paper cites Transcend: Detecting concept drift in malware classification mod- els,.

Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection Transcend: Detecting concept drift in malware classification mod- els,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T04:30:48.435896Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation efa8f771-6c7d-4756-b4e3-4f136c5279fa · outbound

This paper cites Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples.

Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T04:30:48.442583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91d5f779-e692-403e-bef3-0f501d98a45a · outbound

This paper cites Malware makeover: Breaking ml-based static analysis by modifying executable bytes,.

Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection Malware makeover: Breaking ml-based static analysis by modifying executable bytes,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T04:30:48.445540Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31a14ebf-a99e-43c1-9a46-fa95515cee8c · outbound

This paper cites Malware detection by eating a whole exe,.

Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection Malware detection by eating a whole exe,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T04:30:48.448638Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df2d520c-75be-48f4-859a-141c79e1e7e2 · outbound

This paper cites {Explanation-Guided}backdoor poisoning attacks against malware classifiers,.

Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection {Explanation-Guided}backdoor poisoning attacks against malware classifiers,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T04:30:48.451669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b19fd4f1-7fde-4351-a654-b450756b86f7 · outbound

This paper cites MAB-Malware: A Reinforcement Learning Framework for Attacking Static Malware Classifiers.

Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection MAB-Malware: A Reinforcement Learning Framework for Attacking Static Malware Classifiers

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T04:30:48.454670Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c9d3ce0-8eda-421c-b372-c2004f7be0af · outbound

This paper cites Exploring adversarial examples in malware detection,.

Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection Exploring adversarial examples in malware detection,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T04:30:48.457826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b152298f-b5ee-421f-a0d5-5e54b6b08865 · outbound

This paper cites When does machine learning{F AIL}? generalized transferability for evasion and poisoning attacks,.

Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection When does machine learning{F AIL}? generalized transferability for evasion and poisoning attacks,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T04:30:48.460728Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12e353cf-4214-42b5-8d50-a84258c5f26b · outbound

This paper cites Jigsaw puzzle: Selective backdoor attack to subvert malware clas- sifiers,.

Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection Jigsaw puzzle: Selective backdoor attack to subvert malware clas- sifiers,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-10T04:30:48.463294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0f958f9-c123-4c38-9211-640e49a349db · outbound

This paper cites Bodmas: An open dataset for learning based temporal analysis of pe malware,.

Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection Bodmas: An open dataset for learning based temporal analysis of pe malware,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T04:30:48.465912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b64986f6-dc07-48ce-9e6c-4f55c914d9e0 · outbound

This paper cites {CADE}: Detecting and explaining concept drift samples for security applica- tions,.

Empirical Analysis of Evasion and Poisoning Against Malware Data Drift Detection {CADE}: Detecting and explaining concept drift samples for security applica- tions,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T04:30:48.550926Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

No inbound Pith citation observations are available.