Concept Drift Detection and Adaptive Retraining of Malware Classification Models | Paper Citation Record

Observation ee001018-5803-4221-9c5f-95c4e02bb351 · outbound

This paper cites MORPH: Towards Automated Concept Drift Adaptation for Malware Detection.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models MORPH: Towards Automated Concept Drift Adaptation for Malware Detection

Reference 1

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Unavailable: canonical work link unavailable.

Observation e0096458-3d7d-4cba-8704-8db28f0f3376 · outbound

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Concept Drift Detection and Adaptive Retraining of Malware Classification Models Unresolved cited work

Reference 2

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Observation 20ca4efd-ca2f-49fe-a5d4-a9ef0941542a · outbound

This paper cites Springer, 2006.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Springer, 2006

Reference 3

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Observation b8fd5127-34c2-451e-aeb6-48db4042c906 · outbound

This paper cites Analyzing and comparing the effectiveness of malware detection: A study of machine learning approaches.Heliyon, 10(1):e23574, 2024.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Analyzing and comparing the effectiveness of malware detection: A study of machine learning approaches.Heliyon, 10(1):e23574, 2024

Reference 4

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Observation 5ac4890a-8794-4183-97ac-287769415f1a · outbound

This paper cites Ahmed, and Andreas Kassler.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Ahmed, and Andreas Kassler

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:51.649990Z

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Observation 6b1e63d5-b4ff-4bbd-b160-d7c3ab5519d8 · outbound

This paper cites Bertia, Basil Xavier Simon, G.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Bertia, Basil Xavier Simon, G

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:51.617895Z

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Observation 265eabb0-9e1a-4437-92ac-6ce9edba1ede · outbound

This paper cites Collective choice under dichotomous preferences.Journal of Economic Theory, 122(2):165–184, 2005.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Collective choice under dichotomous preferences.Journal of Economic Theory, 122(2):165–184, 2005

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:51.586951Z

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Observation 69c4218e-2276-48a9-8191-3c9af872776c · outbound

This paper cites Random Forests.Machine Learning, 45:5–32, 2001.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Random Forests.Machine Learning, 45:5–32, 2001

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:51.552697Z

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Observation 19dc2da3-63f2-4a2c-9e03-80c1b48df90c · outbound

This paper cites Breunig, Hans-Peter Kriegel, Raymond T.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Breunig, Hans-Peter Kriegel, Raymond T

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:51.515859Z

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Observation bb26c47f-c200-407b-9253-26236a7bbb45 · outbound

This paper cites XGBoost: A scalable tree boosting sys- tem.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models XGBoost: A scalable tree boosting sys- tem

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:51.466093Z

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Observation b0c96032-0250-49f9-98c9-e50fc8108db5 · outbound

This paper cites Monte da Silva, and Bruno Iran Ferreira Maciel.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Monte da Silva, and Bruno Iran Ferreira Maciel

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:51.423161Z

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Observation 2e5dfc59-7181-4dba-b9ab-5af53865a975 · outbound

This paper cites Maximum mean discrepancy for concept drift detection in malware classification models.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Maximum mean discrepancy for concept drift detection in malware classification models

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:51.380099Z

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Observation e0d5f4a7-4ba9-4edb-bb46-62b5e3b32060 · outbound

This paper cites Coello Coello, B.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Coello Coello, B

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:51.341662Z

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Observation b56c9f46-d090-4262-90a2-b3c72aa848ed · outbound

This paper cites Support-vector networks.Machine Learning, 20:273–297, 1995.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Support-vector networks.Machine Learning, 20:273–297, 1995

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:51.307968Z

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Observation ec079430-f7e4-4d40-a0fc-d5f72e5ca08f · outbound

This paper cites Trends in ai inference energy consumption: Beyond the performance-vs- parameter laws of deep learning.Sustainable Computing: Informatics and Systems, 38:100857, 2023.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Trends in ai inference energy consumption: Beyond the performance-vs- parameter laws of deep learning.Sustainable Computing: Informatics and Systems, 38:100857, 2023

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:51.279945Z

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Observation b1e06252-9739-47be-be23-cc849b831af0 · outbound

This paper cites Outlier detection with One-Class SVMs: An application to melanoma progno- sis.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Outlier detection with One-Class SVMs: An application to melanoma progno- sis

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:51.247725Z

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Observation 50005006-c8c2-485a-a4ef-f1ff6300117c · outbound

This paper cites an unresolved cited work.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Unresolved cited work

Reference 17

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Observation f21f2fee-5c76-4748-affa-6939d274e47e · outbound

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Concept Drift Detection and Adaptive Retraining of Malware Classification Models Unresolved cited work

Reference 18

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Observation 733bcb77-9de9-4ce9-8c30-e144cd27f0e0 · outbound

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Concept Drift Detection and Adaptive Retraining of Malware Classification Models Unresolved cited work

Reference 19

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Observation f20f6fae-bf27-4264-a1e5-9060a9a6cd25 · outbound

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Concept Drift Detection and Adaptive Retraining of Malware Classification Models Unresolved cited work

Reference 20

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Observation 9ad50a64-5e72-4231-a384-6cea1a977eae · outbound

This paper cites Borgwardt, Malte J.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Borgwardt, Malte J

Reference 21

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Observation ad79981b-1fed-4fa6-a622-31ce8f8fccc5 · outbound

This paper cites GitHub - aleguma/kronodroid: KronoDroid dataset.https://github.com/aleguma/kronodroid, 2021.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models GitHub - aleguma/kronodroid: KronoDroid dataset.https://github.com/aleguma/kronodroid, 2021

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:51.029707Z

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Observation fdc00c55-08c2-43de-a844-6180512eeb3f · outbound

This paper cites Kron- oDroid: Time-based hybrid-featured dataset for effective android malware de- tection and characterization.Computers & Security, 110:102399, 2021.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Kron- oDroid: Time-based hybrid-featured dataset for effective android malware de- tection and characterization.Computers & Security, 110:102399, 2021

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:51.002453Z

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Observation fb72785b-765d-41fb-b1d3-fccda98117c2 · outbound

This paper cites an unresolved cited work.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Unresolved cited work

Reference 24

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Observation 4826afbe-2139-4213-8359-fd4f55d122b0 · outbound

This paper cites Combating concept drift with explanatory detection and adaptation for android malware classifi- cation.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Combating concept drift with explanatory detection and adaptation for android malware classifi- cation

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:50.909163Z

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Observation 8b682828-8e46-4206-b4a1-d97948d792a8 · outbound

This paper cites an unresolved cited work.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Unresolved cited work

Reference 26

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Observation e11f5a71-e39f-4f97-af54-274b3a7e354a · outbound

This paper cites Ikotun, Absalom E.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Ikotun, Absalom E

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:50.811572Z

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Observation 88b45306-cd2d-493f-9988-3f15a543cb0e · outbound

This paper cites PE header analysis for malware detection.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models PE header analysis for malware detection

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:50.772020Z

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Observation 5b522702-e5a5-4150-883b-dbf5b81291e5 · outbound

This paper cites an unresolved cited work.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Unresolved cited work

Reference 29

Resolution: unresolved; raw_fallback, observed 2026-08-14T10:31:50.734307Z

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Observation 493ffe8c-8a58-43aa-bc5c-b6e3b8012fd4 · outbound

This paper cites XGBoost versus Random Forest.https://www.qwak.com/post/ xgboost-versus-random-forest, 2022.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models XGBoost versus Random Forest.https://www.qwak.com/post/ xgboost-versus-random-forest, 2022

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:50.710598Z

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Observation 2a5a5d93-ddc5-40ce-9369-c5f3f4d2bccd · outbound

This paper cites Revisiting Concept Drift in Windows Malware Detection: Adaptation to Real Drifted Malware with Minimal Samples.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Revisiting Concept Drift in Windows Malware Detection: Adaptation to Real Drifted Malware with Minimal Samples

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-14T10:31:49.008473Z

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Observation 65e25e0c-6502-4e46-b73d-ee02731604e8 · outbound

This paper cites Isolation forest.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Isolation forest

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-14T10:31:49.018440Z

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Observation 4337c3b4-40bb-47f3-babb-5ded16671a0a · outbound

This paper cites Manevitz and Malik Yousef.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Manevitz and Malik Yousef

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:50.641702Z

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Observation 7b58af80-5e20-47ce-9a21-76200bb5697b · outbound

This paper cites Energy Considerations for Large Pretrained Neural Networks.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Energy Considerations for Large Pretrained Neural Networks

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-14T10:31:49.662035Z

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Observation 2410c090-e02e-4030-a0ad-37dfb0255fac · outbound

This paper cites A survey of malware detec- tion techniques based on machine learning.International Journal of Advanced Computer Science and Applications, 10(1), 2019.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models A survey of malware detec- tion techniques based on machine learning.International Journal of Advanced Computer Science and Applications, 10(1), 2019

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:50.610116Z

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Observation 51b41342-6a6b-4b87-9cb7-c70a60aef6be · outbound

This paper cites Cluster analysis and concept drift detection in malware.Journal of Computer Virology and Hacking Techniques, 21, 2025.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Cluster analysis and concept drift detection in malware.Journal of Computer Virology and Hacking Techniques, 21, 2025

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:50.581571Z

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Observation e451c55f-d197-4127-98e6-3e3d1738fca7 · outbound

This paper cites Zubair Rafique, and Juan Caballero.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Zubair Rafique, and Juan Caballero

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:50.554184Z

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Observation ca7e1850-9862-4a56-a7af-cf1734f64222 · outbound

This paper cites an unresolved cited work.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-08-14T10:31:50.510041Z

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Observation 9a7129f9-8ba7-4547-9a0f-5002ccaa0ff0 · outbound

This paper cites Word embedding techniques for malware evo- lution detection.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Word embedding techniques for malware evo- lution detection

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:50.465505Z

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Observation 9185f9d1-ec08-4a17-a353-cadd107724e9 · outbound

This paper cites an unresolved cited work.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Unresolved cited work

Reference 40

Resolution: unresolved; raw_fallback, observed 2026-08-14T10:31:50.386502Z

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Observation ab840078-68b4-4ad8-b098-ac2c87ecc2da · outbound

This paper cites an unresolved cited work.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Unresolved cited work

Reference 41

Resolution: unresolved; raw_fallback, observed 2026-08-14T10:31:50.347139Z

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Observation 388d2118-d7aa-427e-b0d3-208e66d61f55 · outbound

This paper cites Choudhary, K.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Choudhary, K

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:50.316880Z

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Observation cd598885-e4b0-4379-8814-34f450cfcb2a · outbound

This paper cites Ross, Niall M.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Ross, Niall M

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:50.279513Z

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Observation c4843a17-530e-4094-9831-dba69a0addbe · outbound

This paper cites Rousseeuw and Katrien Van Driessen.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Rousseeuw and Katrien Van Driessen

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:50.233328Z

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Observation c2ffa814-3e14-4733-950c-fe51b6da69de · outbound

This paper cites Platt, John Shawe-Taylor, Alex J.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Platt, John Shawe-Taylor, Alex J

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:50.211483Z

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Observation ac41df89-7618-4db9-a8f1-abdaf5e57a07 · outbound

This paper cites novelty and outlier detection.https://scikit-learn.org/ stable/modules/outlier_detection.html.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models novelty and outlier detection.https://scikit-learn.org/ stable/modules/outlier_detection.html

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:50.179405Z

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Observation 7bb2190b-066e-4b2d-b2ea-2970c5b5ee45 · outbound

This paper cites McCann, Ying Huang, Wei Wang, and Jun Kong.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models McCann, Ying Huang, Wei Wang, and Jun Kong

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:50.147439Z

Source-reported events for the cited work

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Observation 7e73a393-64d3-42aa-9e5d-a105385dd04a · outbound

This paper cites neural_network.MLPClassifier.html, 2010.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models neural_network.MLPClassifier.html, 2010

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:50.121502Z

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Observation 278c0863-00c3-48c6-8b54-41863dfeef90 · outbound

This paper cites A fuzzy drift corre- lation matrix for multiple data stream regression.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models A fuzzy drift corre- lation matrix for multiple data stream regression

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:50.084769Z

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Observation 29b47034-1b3f-4fa9-a656-847cdeb57da4 · outbound

This paper cites Chapman and Hall/CRC, Boca Raton, second edition, 2022.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Chapman and Hall/CRC, Boca Raton, second edition, 2022

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:50.053061Z

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Observation 9f54a1a9-14a5-473f-a215-2ca2b94bcbef · outbound

This paper cites an unresolved cited work.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Unresolved cited work

Reference 51

Resolution: unresolved; raw_fallback, observed 2026-08-14T10:31:50.009098Z

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Observation 23f2a9a3-de95-4ed8-8c06-b9c4359be326 · outbound

This paper cites Papadopoulos, and Yannis Manolopoulos.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Papadopoulos, and Yannis Manolopoulos

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:49.972597Z

Source-reported events for the cited work

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Observation 52c89bb6-5a12-46d0-8f15-d8977627f00a · outbound

This paper cites Machine learning for malware evolu- tion detection.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Machine learning for malware evolu- tion detection

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:49.945989Z

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 bc8250b2-d6cd-425c-809f-b094dedf8594 · outbound

This paper cites Alibi detect: Algorithms for outlier, adversarial and drift detection, 2019.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Alibi detect: Algorithms for outlier, adversarial and drift detection, 2019

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:49.911387Z

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 2a3cdc50-b56a-48dc-ae1a-d10c316d7715 · outbound

This paper cites Detecting malware evolution using support vector machines.Expert Systems with Applications, 143:113022, 2020.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Detecting malware evolution using support vector machines.Expert Systems with Applications, 143:113022, 2020

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:49.885459Z

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 35a8c1a9-45ae-48e4-9c63-154d0ee8fbc5 · outbound

This paper cites Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-14T10:31:49.252042Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 065924f4-5a98-485d-8683-2e255b63193b · outbound

This paper cites What is a support vector ma- chine (SVM)?https://www.techtarget.com/whatis/definition/support- vector-machine-SVM, 2023.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models What is a support vector ma- chine (SVM)?https://www.techtarget.com/whatis/definition/support- vector-machine-SVM, 2023

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:49.861642Z

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 7f196d2e-a6c9-47fb-9dfc-5ff2625d6811 · outbound

This paper cites MOEA/D: A multiobjective evolutionary algorithm based on decomposition.IEEE Transactions on Evolutionary Computation, 11(6):712–731, 2007.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models MOEA/D: A multiobjective evolutionary algorithm based on decomposition.IEEE Transactions on Evolutionary Computation, 11(6):712–731, 2007

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-14T10:31:49.266828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1427f170-dd16-4645-8e52-bc9284f15cee · outbound

This paper cites Adaptive online incremental learning for evolving data streams.Applied Soft Computing, 105:107255, 2021.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models Adaptive online incremental learning for evolving data streams.Applied Soft Computing, 105:107255, 2021

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:49.812192Z

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 ca5d0951-fd2e-4e57-87ec-77a0afdc9f47 · outbound

This paper cites MANAGE: A novel malware evolution model based on digital genes.

Concept Drift Detection and Adaptive Retraining of Malware Classification Models MANAGE: A novel malware evolution model based on digital genes

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T10:31:49.779526Z

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.

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