Paper Citation Record · LEDGER

Addressing malware family concept drift with triplet autoencoder

As of 10 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2507.00348.

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

pith.paper-citation-record.v1
2507.00348 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T21:24:28.175056Z

measured 46 of 46 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 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

46 of 46 outbound references displayed

verified exact0
verified fuzzy41
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation daf43dc6-58ab-4def-b233-a60960efa9dd · outbound

This paper cites Learning under concept drift: A review,.

Addressing malware family concept drift with triplet autoencoder Learning under concept drift: A review,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:35.132020Z

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.

Observation 3da4f227-57c4-4c6b-84e3-c7d90c2d6040 · outbound

This paper cites Malware statistics & trends report,.

Addressing malware family concept drift with triplet autoencoder Malware statistics & trends report,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:34.970006Z

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.

Observation f26d1dfc-2f30-49c5-8475-a0c20670d5e7 · outbound

This paper cites Tesseract: Eliminating experimental bias in malware classification across space and time,.

Addressing malware family concept drift with triplet autoencoder Tesseract: Eliminating experimental bias in malware classification across space and time,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:34.847594Z

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.

Observation 2f0874da-a30e-4c0e-863e-ce0b4f0d7c7a · outbound

This paper cites Transcend: Detecting concept drift in malware classification models,.

Addressing malware family concept drift with triplet autoencoder Transcend: Detecting concept drift in malware classification models,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:34.693112Z

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.

Observation 23677695-f2e4-4ca7-8297-757b18ffb408 · outbound

This paper cites Towards open set deep networks,.

Addressing malware family concept drift with triplet autoencoder Towards open set deep networks,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:34.593675Z

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.

Observation cb39a577-d71f-4b32-b74c-dd3091daa292 · outbound

This paper cites A simple unified frame- work for detecting out-of-distribution samples and adversarial attacks,.

Addressing malware family concept drift with triplet autoencoder A simple unified frame- work for detecting out-of-distribution samples and adversarial attacks,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:34.448546Z

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.

Observation fa35d2d4-7afc-49a6-906b-9e59bec608a4 · outbound

This paper cites Android malware detection: Mission accomplished? a review of open challenges and future per- spectives,.

Addressing malware family concept drift with triplet autoencoder Android malware detection: Mission accomplished? a review of open challenges and future per- spectives,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:34.293761Z

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.

Observation 77dab403-ddef-4a03-931b-3cda12b7128a · outbound

This paper cites Novel feature extraction, selection and fusion for effective malware family classification,.

Addressing malware family concept drift with triplet autoencoder Novel feature extraction, selection and fusion for effective malware family classification,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:34.047124Z

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.

Observation e47cc1cf-0881-47bb-8a70-5f03cc4ea9ca · outbound

This paper cites Malware detection based on mining api calls,.

Addressing malware family concept drift with triplet autoencoder Malware detection based on mining api calls,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:33.861866Z

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.

Observation 3e97df00-b435-42be-af73-bc463c1a8b90 · outbound

This paper cites Byte level n–gram analysis for malware detection,.

Addressing malware family concept drift with triplet autoencoder Byte level n–gram analysis for malware detection,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:33.641254Z

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.

Observation c15a45e1-467e-4c3e-b9ef-24e62d8791cc · outbound

This paper cites Malware detection and classifica- tion based on n-grams attribute similarity,.

Addressing malware family concept drift with triplet autoencoder Malware detection and classifica- tion based on n-grams attribute similarity,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:33.443859Z

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.

Observation 6d42dfca-8a08-411e-8b3c-3779d8b3f05f · outbound

This paper cites Deep android malware detection,.

Addressing malware family concept drift with triplet autoencoder Deep android malware detection,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:33.261688Z

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.

Observation 53601bd8-001b-4d32-8376-6729ff5aaca1 · outbound

This paper cites Sequential op- code embedding-based malware detection method,.

Addressing malware family concept drift with triplet autoencoder Sequential op- code embedding-based malware detection method,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:33.082834Z

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.

Observation 4273bd73-ceae-4da6-89e5-4cd88a616410 · outbound

This paper cites Malware detection based on deep learn- ing algorithm,.

Addressing malware family concept drift with triplet autoencoder Malware detection based on deep learn- ing algorithm,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:32.876383Z

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.

Observation 85564bd2-3c10-4cf7-a186-6a084c47fe9a · outbound

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

Addressing malware family concept drift with triplet autoencoder Drebin: Effective and explainable detection of android malware in your pocket.,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:32.684049Z

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.

Observation 10bb0485-9b61-44cd-aa91-09c322fd179c · outbound

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

Addressing malware family concept drift with triplet autoencoder EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T21:24:24.305129Z

Source-reported events for the cited work

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Observation f1f13758-ddc6-4e87-a011-64b1ff7ab7f8 · outbound

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

Addressing malware family concept drift with triplet autoencoder Bodmas: An open dataset for learning based temporal analysis of pe malware,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:32.546103Z

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.

Observation 866af7a4-b19a-49d8-92aa-c3e4d75c0f7a · outbound

This paper cites Maar: Robust features to detect malicious activity based on api calls, their arguments and return values,.

Addressing malware family concept drift with triplet autoencoder Maar: Robust features to detect malicious activity based on api calls, their arguments and return values,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:32.389295Z

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.

Observation 8ea392d8-a515-40d4-96b5-a8eee94f0946 · outbound

This paper cites Malware detection and classification based on extraction of api sequences,.

Addressing malware family concept drift with triplet autoencoder Malware detection and classification based on extraction of api sequences,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:32.269841Z

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.

Observation c66656f8-6d30-4c4d-a886-0817ba93704a · outbound

This paper cites Detecting obfus- cated malware using reduced opcode set and optimised runtime trace,.

Addressing malware family concept drift with triplet autoencoder Detecting obfus- cated malware using reduced opcode set and optimised runtime trace,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:32.115445Z

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.

Observation 02ec6da5-3505-4d7e-8bff-dba41a53271c · outbound

This paper cites Network malware classification comparison using dpi and flow packet headers,.

Addressing malware family concept drift with triplet autoencoder Network malware classification comparison using dpi and flow packet headers,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:31.979431Z

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.

Observation f5fc26f6-bc4c-4195-b172-239b17fec7a4 · outbound

This paper cites Malicious software classification using transfer learning of resnet-50 deep neural network,.

Addressing malware family concept drift with triplet autoencoder Malicious software classification using transfer learning of resnet-50 deep neural network,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:31.830242Z

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.

Observation 3a3d06bd-593f-456d-82f0-b4d6e3c7ea24 · outbound

This paper cites In Defense of the Triplet Loss for Person Re-Identification.

Addressing malware family concept drift with triplet autoencoder In Defense of the Triplet Loss for Person Re-Identification

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T21:24:25.278451Z

Source-reported events for the cited work

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Observation e916bd8d-fa39-4ede-83d1-f74332c9bb63 · outbound

This paper cites Triplet loss in siamese network for object tracking,.

Addressing malware family concept drift with triplet autoencoder Triplet loss in siamese network for object tracking,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:31.641987Z

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.

Observation 81b39c97-d5de-4ab7-b662-5f2dc19e54cb · outbound

This paper cites A zero-shot deep metric learning approach to brain–computer interfaces for image retrieval,.

Addressing malware family concept drift with triplet autoencoder A zero-shot deep metric learning approach to brain–computer interfaces for image retrieval,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:31.536173Z

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.

Observation 37d6835d-ccbc-4ee4-8385-8de4f5c33da0 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Addressing malware family concept drift with triplet autoencoder Representation Learning with Contrastive Predictive Coding

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T21:24:25.662754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 733a8fd9-329c-48fc-8347-9c2287a340ae · outbound

This paper cites Metric learning- based multimodal audio-visual emotion recognition,.

Addressing malware family concept drift with triplet autoencoder Metric learning- based multimodal audio-visual emotion recognition,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:31.477020Z

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.

Observation e33bdbb9-9350-4ae5-819e-86f39e2d4765 · outbound

This paper cites In defence of metric learning for speaker recognition.

Addressing malware family concept drift with triplet autoencoder In defence of metric learning for speaker recognition

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T21:24:25.971627Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 560dc32d-29d3-4e5f-8ced-b623fd0ae49b · outbound

This paper cites Multi-instance multi- label distance metric learning for genome-wide protein func- tion prediction,.

Addressing malware family concept drift with triplet autoencoder Multi-instance multi- label distance metric learning for genome-wide protein func- tion prediction,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:31.416088Z

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.

Observation 0908475a-f05f-4880-a916-aca98aeb46fe · outbound

This paper cites A novel drug repositioning approach based on collaborative metric learning,.

Addressing malware family concept drift with triplet autoencoder A novel drug repositioning approach based on collaborative metric learning,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:31.359283Z

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.

Observation f42a27c1-9b3e-4b1c-8896-cc76654b3e98 · outbound

This paper cites Contrastive learning for robust android malware familial classification,.

Addressing malware family concept drift with triplet autoencoder Contrastive learning for robust android malware familial classification,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:31.304388Z

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.

Observation eb2bae39-e00a-4099-930a-f9412e3edd64 · outbound

This paper cites Application of distance metric learning to automated malware detection,.

Addressing malware family concept drift with triplet autoencoder Application of distance metric learning to automated malware detection,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:31.244624Z

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.

Observation d8b85695-ccce-421c-a081-26520e140d42 · outbound

This paper cites Fewm- hgcl: Few-shot malware variants detection via heterogeneous graph contrastive learning,.

Addressing malware family concept drift with triplet autoencoder Fewm- hgcl: Few-shot malware variants detection via heterogeneous graph contrastive learning,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:31.184722Z

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.

Observation 54e9a541-f18a-4470-bfe7-d581875df7fa · outbound

This paper cites Autoencoder- based deep metric learning for network intrusion detection,.

Addressing malware family concept drift with triplet autoencoder Autoencoder- based deep metric learning for network intrusion detection,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:31.122968Z

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.

Observation 7b69e132-74b4-4976-b192-310f086fdcf0 · outbound

This paper cites Tracking concept drift in malware families,.

Addressing malware family concept drift with triplet autoencoder Tracking concept drift in malware families,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:31.060646Z

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.

Observation 12fe764d-e597-4288-91ae-b1aa612b2fc1 · outbound

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

Addressing malware family concept drift with triplet autoencoder Transcending transcend: Revisiting malware classification in the presence of concept drift,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:30.762179Z

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.

Observation 0cc4d6c2-177a-4dd7-89ae-737fae0d4f1c · outbound

This paper cites Cade: Detecting and explaining concept drift samples for security applications,.

Addressing malware family concept drift with triplet autoencoder Cade: Detecting and explaining concept drift samples for security applications,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:30.416226Z

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.

Observation 31fa12e7-0792-40f0-9608-eec5d7f5c464 · outbound

This paper cites Insomnia: Towards concept-drift robustness in network intrusion detection,.

Addressing malware family concept drift with triplet autoencoder Insomnia: Towards concept-drift robustness in network intrusion detection,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T21:24:27.129629Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13dbe3f4-b540-4c68-b66a-fbd8b5f54305 · outbound

This paper cites Temporal analysis of dis- tribution shifts in malware classification for digital forensics,.

Addressing malware family concept drift with triplet autoencoder Temporal analysis of dis- tribution shifts in malware classification for digital forensics,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:30.041048Z

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.

Observation 3319fb0d-3f8c-4dcf-8346-9fb77e45985d · outbound

This paper cites Deep metric learning: A survey,.

Addressing malware family concept drift with triplet autoencoder Deep metric learning: A survey,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:29.686585Z

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.

Observation ee246fe0-6992-4398-af4d-3911426eb9a5 · outbound

This paper cites The curse (s) of dimension- ality,.

Addressing malware family concept drift with triplet autoencoder The curse (s) of dimension- ality,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:29.446959Z

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.

Observation 7587d0e6-c042-4ff0-bd24-93ad73fd57bf · outbound

This paper cites Dbscan revisited, revisited: Why and how you should (still) use dbscan,.

Addressing malware family concept drift with triplet autoencoder Dbscan revisited, revisited: Why and how you should (still) use dbscan,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:29.226658Z

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.

Observation 03e51e84-db58-4670-86b2-f44eb5579e92 · outbound

This paper cites The k-means algo- rithm: A comprehensive survey and performance evaluation,.

Addressing malware family concept drift with triplet autoencoder The k-means algo- rithm: A comprehensive survey and performance evaluation,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:29.052189Z

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.

Observation da42a5c0-e3fa-4b0f-b718-1fc7ca4b28a3 · outbound

This paper cites A density-based algorithm for discovering clusters in large spatial databases with noise,.

Addressing malware family concept drift with triplet autoencoder A density-based algorithm for discovering clusters in large spatial databases with noise,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:28.848425Z

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.

Observation 4107eb0c-a581-47c3-8720-e4996b8d5bd0 · outbound

This paper cites Fesa: Feature selection architecture for ransomware detection under concept drift,.

Addressing malware family concept drift with triplet autoencoder Fesa: Feature selection architecture for ransomware detection under concept drift,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:28.598824Z

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.

Observation ceafdbab-16ce-4eb3-b748-ce7f24bb66c1 · outbound

This paper cites Visualizing data using t- sne.,.

Addressing malware family concept drift with triplet autoencoder Visualizing data using t- sne.,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:24:28.428957Z

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.

Pith citing papers

No inbound Pith citation observations are available.