Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection | Paper Citation Record

Observation b505f099-5c71-4504-951e-16e28956e639 · outbound

This paper cites Mobile threat report for q1 2025 — securelist,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Mobile threat report for q1 2025 — securelist,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:21:40.247026Z

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Observation 1da4cd9e-98da-4693-98db-a3258ae48df0 · outbound

This paper cites Banking data theft attacks on smartphones triple in 2024, kaspersky reports,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Banking data theft attacks on smartphones triple in 2024, kaspersky reports,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:21:40.240879Z

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Observation a1163d74-33ea-4158-816e-a0dd9d72da1a · outbound

This paper cites Detecting android malware leveraging text semantics of network flows,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Detecting android malware leveraging text semantics of network flows,

Reference 3

Resolution: malformed identifier; no resolver link, observed 2026-08-06T11:21:38.087835Z

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Observation 9d0df56a-e660-45b8-b268-cb00cbbf56c2 · outbound

This paper cites Droidcat: Effective android malware detection and categorization via app-level profiling,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Droidcat: Effective android malware detection and categorization via app-level profiling,

Reference 4

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T11:21:40.034526Z

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Observation 1a642ad1-1eb6-4eab-b919-1d32c42157f5 · outbound

This paper cites Permpair: Android malware detection using permission pairs,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Permpair: Android malware detection using permission pairs,

Reference 5

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T11:21:39.962899Z

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

Observation 57826746-16b0-463b-8b7c-a39bad725b17 · outbound

This paper cites Recent advances in android mobile malware detection: A systematic literature review,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Recent advances in android mobile malware detection: A systematic literature review,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T11:21:38.097885Z

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Observation eb884afe-5a64-4c4f-90f6-f6d8990b2fd5 · outbound

This paper cites Limondroid: a system coupling three signature- based schemes for profiling android malware,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Limondroid: a system coupling three signature- based schemes for profiling android malware,

Reference 7

Resolution: verified exact; doi, observed 2026-08-06T11:21:38.400009Z

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Observation 87362800-846f-4bbf-b018-6de4ad7d46ba · outbound

This paper cites A framework for detection of android malware using static features,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection A framework for detection of android malware using static features,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:21:40.234694Z

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Observation 64603545-2ad2-400a-b574-807f7b3f2fde · outbound

This paper cites Robust deep learning early alarm prediction model based on the behavioural smell for android malware,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Robust deep learning early alarm prediction model based on the behavioural smell for android malware,

Reference 9

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T11:21:39.827570Z

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Observation 5040d02e-ceb9-4e6d-b46b-bd151e3a6c41 · outbound

This paper cites An early detection of android malware using system calls based machine learning model,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection An early detection of android malware using system calls based machine learning model,

Reference 10

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T11:21:39.766372Z

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 c0f19500-b6f6-46f6-b68e-86c6059054ef · outbound

This paper cites Continuous learning for android malware detection,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Continuous learning for android malware detection,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:21:40.227684Z

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 3363960c-5a04-4130-9e9d-76d4722c15bc · outbound

This paper cites A unifying view on dataset shift in classification,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection A unifying view on dataset shift in classification,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T11:21:38.115256Z

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Observation c660625d-9ce8-4f76-8e45-d9606e7b6787 · outbound

This paper cites TESSERACT: eliminating experimental bias in malware classification across space and time,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection TESSERACT: eliminating experimental bias in malware classification across space and time,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:21:40.212741Z

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Observation 3d03d001-b905-4019-8ff0-552ebe8ab3ce · outbound

This paper cites Transcending TRANSCEND: revisiting malware classification in the presence of concept drift,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Transcending TRANSCEND: revisiting malware classification in the presence of concept drift,

Reference 14

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T11:21:39.695112Z

Source-reported events for the cited work

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Observation 777901e7-ed32-4684-b1f2-875f9b0b57c7 · outbound

This paper cites Kronodroid: Time-based hybrid-featured dataset for effective android malware detection and characterization,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Kronodroid: Time-based hybrid-featured dataset for effective android malware detection and characterization,

Reference 15

Resolution: malformed identifier; no resolver link, observed 2026-08-06T11:21:38.124164Z

Source-reported events for the cited work

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Observation 5591f421-3667-4387-8bcb-defe022b8d84 · outbound

This paper cites Troid: Temporal and cross-sectional android dataset and its applications,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Troid: Temporal and cross-sectional android dataset and its applications,

Reference 16

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T11:21:40.199335Z

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 32a254c7-4f8f-455c-82d2-fb85a1f613e0 · outbound

This paper cites Incremental learning from noisy data,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Incremental learning from noisy data,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T11:21:38.128761Z

Source-reported events for the cited work

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Observation fad8363f-e5dc-47c1-b885-eed84bd4949f · outbound

This paper cites Concept drift adaptation methods under the deep learning framework: A literature review,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Concept drift adaptation methods under the deep learning framework: A literature review,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:21:40.193276Z

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 a8a976f2-b3a6-4f9d-a793-bcc6ec773269 · outbound

This paper cites an unresolved cited work.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Unresolved cited work

Reference 19

Resolution: verified exact; doi, observed 2026-08-06T11:21:38.384489Z

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 8b5e5ad7-5e40-439f-8044-f0d24f22d9c3 · outbound

This paper cites Android malware detection techniques in traditional and cloud computing platforms: A state-of-the-art survey,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Android malware detection techniques in traditional and cloud computing platforms: A state-of-the-art survey,

Reference 20

Resolution: verified exact; doi, observed 2026-08-06T11:21:38.377609Z

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 3120562d-2d89-4bf1-9c68-3421af06c8b7 · outbound

This paper cites A systematic overview of android malware detection,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection A systematic overview of android malware detection,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T11:21:38.138455Z

Source-reported events for the cited work

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Observation be51f2af-a168-45e6-ac90-08fdb15d5640 · outbound

This paper cites Adaptive android mal- ware signature detection,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Adaptive android mal- ware signature detection,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:21:40.185449Z

Source-reported events for the cited work

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Observation d69debf0-7c63-4c78-8ce6-4f5b8a0bf2eb · outbound

This paper cites Permission based malware detection in android devices,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Permission based malware detection in android devices,

Reference 23

Resolution: verified exact; doi, observed 2026-08-06T11:21:38.370829Z

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 7b7add75-94e8-46b9-b0a5-3941c622a22f · outbound

This paper cites Android malware detection based on composition ratio of permission pairs,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Android malware detection based on composition ratio of permission pairs,

Reference 24

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T11:21:39.493984Z

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 21162462-e9d7-4ab2-bd45-649e41b01d1e · outbound

This paper cites Malware detection: A framework for reverse engineered android applications through machine learning algorithms,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Malware detection: A framework for reverse engineered android applications through machine learning algorithms,

Reference 25

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T11:21:39.439877Z

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 0f151656-1228-4869-b4f5-31bce1749edf · outbound

This paper cites An android malware detection approach based on static feature analysis using machine learning algorithms,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection An android malware detection approach based on static feature analysis using machine learning algorithms,

Reference 26

Resolution: verified exact; doi, observed 2026-08-06T11:21:38.363101Z

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 a3eafd94-0e6f-475b-9b83-5923cc4677b0 · outbound

This paper cites Multi- view deep learning for zero-day android malware detection,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Multi- view deep learning for zero-day android malware detection,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T11:21:38.152044Z

Source-reported events for the cited work

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Observation 3e143b8d-9e9f-45fe-8814-0ebcbfa7f2d6 · outbound

This paper cites N- gram, semantic-based neural network for mobile malware network traffic detection,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection N- gram, semantic-based neural network for mobile malware network traffic detection,

Reference 28

Resolution: verified exact; doi, observed 2026-08-06T11:21:38.355778Z

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 efc53269-b11d-4265-9435-c8630ec222c5 · outbound

This paper cites An efficient android malware detection system based on method-level behavioral semantic analysis,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection An efficient android malware detection system based on method-level behavioral semantic analysis,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:21:40.178390Z

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 91e40bde-029a-431f-a05b-be6c6685e437 · outbound

This paper cites A malware detection approach using autoencoder in deep learning,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection A malware detection approach using autoencoder in deep learning,

Reference 30

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T11:21:39.310960Z

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 1da69c67-952c-41b9-8899-0579d02f2fc6 · outbound

This paper cites Android malware detection based on image-based features and machine learning techniques,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Android malware detection based on image-based features and machine learning techniques,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:21:40.166806Z

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 43aafd27-c2db-4546-bc94-0dc4d9ef8f92 · outbound

This paper cites MCNN-LSTM: combining CNN and LSTM to classify multi-class text in imbalanced news data,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection MCNN-LSTM: combining CNN and LSTM to classify multi-class text in imbalanced news data,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T11:21:38.164878Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b98fd28-c623-4e79-9a29-03344ca1ac51 · outbound

This paper cites MalBERT: Using Transformers for Cybersecurity and Malicious Software Detection.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection MalBERT: Using Transformers for Cybersecurity and Malicious Software Detection

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-06T11:21:38.166867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation babfa0cc-689b-4c1e-b5b1-fd12335b8ec8 · outbound

This paper cites Android malware detection through a pre-trained model for code understanding,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Android malware detection through a pre-trained model for code understanding,

Reference 34

Resolution: verified exact; doi, observed 2026-08-06T11:21:38.343595Z

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 ad60a0c6-4b13-467f-9234-e205694f9636 · outbound

This paper cites Multimodal fusion for android malware detection based on large pre-trained models,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Multimodal fusion for android malware detection based on large pre-trained models,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T11:21:38.171077Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dcdff157-569d-4070-bac3-24c1e33fb678 · outbound

This paper cites Unsupervised anomaly-based malware detection using hardware features,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Unsupervised anomaly-based malware detection using hardware features,

Reference 36

Resolution: verified exact; doi, observed 2026-08-06T11:21:38.336129Z

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 2cf6fce8-d3dd-4291-b963-e28dc87af75f · outbound

This paper cites A Contemporary Survey of Large Language Model Assisted Program Analysis.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection A Contemporary Survey of Large Language Model Assisted Program Analysis

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T11:21:38.174929Z

Source-reported events for the cited work

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Observation 06a329de-618e-4063-b0ee-c47458e45284 · outbound

This paper cites Exploring LLMs for Malware Detection: Review, Framework Design, and Countermeasure Approaches.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Exploring LLMs for Malware Detection: Review, Framework Design, and Countermeasure Approaches

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T11:21:38.177587Z

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Observation 98242c6c-d3df-47aa-9230-7cda049c26d6 · outbound

This paper cites From large to mammoth: A comparative evaluation of large language models in vulnerability detection,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection From large to mammoth: A comparative evaluation of large language models in vulnerability detection,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:21:40.156631Z

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 09b28e30-96be-476a-a893-2563c1419171 · outbound

This paper cites A novel permission-based android malware detection system using feature selection based on linear regression,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection A novel permission-based android malware detection system using feature selection based on linear regression,

Reference 40

Resolution: verified exact; doi, observed 2026-08-06T11:21:38.316385Z

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 1e6cceff-71c7-4d9b-810d-5f530808231e · outbound

This paper cites Malware detection in android based on dynamic analysis,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Malware detection in android based on dynamic analysis,

Reference 41

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T11:21:39.127017Z

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 dcc70e2f-9ded-466f-940d-594681cd2a23 · outbound

This paper cites Dynamic android malware analysis with de-identification of personal identifiable informa- tion,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Dynamic android malware analysis with de-identification of personal identifiable informa- tion,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:21:40.149194Z

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 f62c2ce2-f782-45c3-a42b-0c6bbbfe5504 · outbound

This paper cites Dynamic permissions based android malware detection using machine learning techniques,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Dynamic permissions based android malware detection using machine learning techniques,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:21:40.141632Z

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 b9715019-98fd-4cc2-97f9-f7784c1acdf4 · outbound

This paper cites Dynamic mobile malware detection through system call-based image representation,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Dynamic mobile malware detection through system call-based image representation,

Reference 44

Resolution: verified exact; doi, observed 2026-08-06T11:21:38.309903Z

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 30576eb9-c06e-49af-b4e0-c51d8b1b708f · outbound

This paper cites Dynamic android malware category classification us- ing semi-supervised deep learning,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Dynamic android malware category classification us- ing semi-supervised deep learning,

Reference 45

Resolution: verified exact; raw_fallback, observed 2026-08-06T11:21:39.056663Z

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 eb74f6df-fef9-4e7b-8900-0b609b099640 · outbound

This paper cites Dynamic detection of mobile malware using smartphone data and machine learning,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Dynamic detection of mobile malware using smartphone data and machine learning,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:21:40.133761Z

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 e4ca6c0d-6ce0-4e0e-8585-14c3c033cc69 · outbound

This paper cites You are what the permissions told me! android malware detection based on hybrid tactics,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection You are what the permissions told me! android malware detection based on hybrid tactics,

Reference 47

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T11:21:38.976478Z

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 a4fad1b3-04b1-4fa8-afee-0825fde22f33 · outbound

This paper cites Detection and preven- tion of android malware thru permission analysis,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Detection and preven- tion of android malware thru permission analysis,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:21:40.125737Z

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 70dad976-9ade-4be7-8f8b-3250dc5add66 · outbound

This paper cites Hybrid sequence-based android malware detection using natural language processing,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Hybrid sequence-based android malware detection using natural language processing,

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T11:21:38.200048Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 791a0d15-3a40-4249-a1a2-98e5665af010 · outbound

This paper cites Signature based malicious behavior detection in android,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Signature based malicious behavior detection in android,

Reference 50

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T11:21:40.118670Z

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 7ded591a-557d-4c06-9b84-29dcde314d81 · outbound

This paper cites A novel dynamic android malware detection system with ensemble learning,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection A novel dynamic android malware detection system with ensemble learning,

Reference 51

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T11:21:38.909035Z

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 82d4b9a7-b038-4943-adb0-86bdfff6625e · outbound

This paper cites Andro-dumpsys: Anti-malware system based on the similarity of malware creator and malware centric information,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Andro-dumpsys: Anti-malware system based on the similarity of malware creator and malware centric information,

Reference 52

Resolution: verified exact; doi, observed 2026-08-06T11:21:38.299607Z

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 cdc9748b-f951-4fb7-ba4e-c900c46d5a17 · outbound

This paper cites A survey on concept drift adaptation,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection A survey on concept drift adaptation,

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-06T11:21:38.208673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9479753-2586-45e9-aa49-a494039b22b7 · outbound

This paper cites The concept drift problem in android malware detection and its solution,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection The concept drift problem in android malware detection and its solution,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:21:40.112123Z

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 6e0cc7fd-425f-4de5-bb27-75460129ad76 · outbound

This paper cites Is it overkill? analyzing feature-space concept drift in malware detectors,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Is it overkill? analyzing feature-space concept drift in malware detectors,

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-06T11:21:38.214445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a53e7b0-4161-45a6-9d01-9919a706efc5 · outbound

This paper cites Corrigendum to concept drift and cross-device behavior: Challenges and implications for effective android malware detection computers & security, volume 120, 102757,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Corrigendum to concept drift and cross-device behavior: Challenges and implications for effective android malware detection computers & security, volume 120, 102757,

Reference 56

Resolution: malformed identifier; no resolver link, observed 2026-08-06T11:21:38.216437Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91920323-0481-48fb-b60d-b37cdd5e2122 · outbound

This paper cites On the relativity of time: Implications and challenges of data drift on long-term effective android malware detection,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection On the relativity of time: Implications and challenges of data drift on long-term effective android malware detection,

Reference 57

Resolution: malformed identifier; no resolver link, observed 2026-08-06T11:21:38.219523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7386eccc-e91c-4bfb-9606-48630242a1b7 · outbound

This paper cites Drift forensics of malware classifiers,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Drift forensics of malware classifiers,

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T11:21:38.221450Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d236e9fe-46db-4178-9c7e-0295433226ff · outbound

This paper cites Burning the Adversarial Bridges: Robust Windows Malware Detection Against Binary-level Mutations.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Burning the Adversarial Bridges: Robust Windows Malware Detection Against Binary-level Mutations

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T11:21:38.224057Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b318c6bc-c92e-4507-8a2d-152459796163 · outbound

This paper cites Systematically evaluating the robustness of ml-based iot malware detection systems,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Systematically evaluating the robustness of ml-based iot malware detection systems,

Reference 60

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T11:21:38.599133Z

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 21b5fe17-74dc-4b08-abef-72f8b30cb530 · outbound

This paper cites Fast & furious: On the modelling of malware detection as an evolving data stream,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Fast & furious: On the modelling of malware detection as an evolving data stream,

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T11:21:38.229289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0704d7df-c4dd-499c-afdc-3cc820edaa01 · outbound

This paper cites LAMD: Context-driven Android Malware Detection and Classification with LLMs.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection LAMD: Context-driven Android Malware Detection and Classification with LLMs

Reference 62

Resolution: unresolved; no resolver link, observed 2026-08-06T11:21:38.232026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 006d7145-542e-4578-973f-51572ab70f11 · outbound

This paper cites Together ai – the ai acceleration cloud - fast inference, fine-tuning & training,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Together ai – the ai acceleration cloud - fast inference, fine-tuning & training,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:21:40.104455Z

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 183f5676-4ad0-4f90-ab43-ad53644d328c · outbound

This paper cites An lstm-based malware detection using transfer learning,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection An lstm-based malware detection using transfer learning,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:21:40.096096Z

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 197948a0-3f28-41bc-a43a-831c54e47e71 · outbound

This paper cites An effectiveness analysis of transfer learning for the concept drift problem in malware detection,.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection An effectiveness analysis of transfer learning for the concept drift problem in malware detection,

Reference 65

Resolution: malformed identifier; no resolver link, observed 2026-08-06T11:21:38.240021Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 226b2c38-9cbb-4847-a85a-df647921bbbb · outbound

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

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection MORPH: Towards Automated Concept Drift Adaptation for Malware Detection

Reference 66

Resolution: verified exact; local_arxiv, observed 2026-08-06T11:21:38.268518Z

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 9edc5958-42ec-4e58-909a-f27dace18d7a · outbound

This paper cites Available: https://www.usenix.org/conference/ usenixsecurity19/presentation/pendlebury.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Available: https://www.usenix.org/conference/ usenixsecurity19/presentation/pendlebury

Reference 746

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:21:40.205275Z

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 75e69d90-ec66-4c09-a198-d91fa4390124 · outbound

This paper cites Available: https://www.usenix.org/conference/ usenixsecurity23/presentation/chen-yizheng.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Available: https://www.usenix.org/conference/ usenixsecurity23/presentation/chen-yizheng

Reference 1144

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T11:21:40.220216Z

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 a94a4d47-af56-4708-9e35-e44675a9759b · outbound

This paper cites Available: https://doi.org/10.1155/2017/4956386.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Available: https://doi.org/10.1155/2017/4956386

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-06T11:21:38.212328Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f1787ed-269e-4b99-aa9c-1cfbb68b463a · outbound

This paper cites Available: https://doi.org/10.1109/ACCESS.2019.

Empirical Evaluation of Concept Drift in ML-Based Android Malware Detection Available: https://doi.org/10.1109/ACCESS.2019

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-06T11:21:38.159204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers