Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance | Paper Citation Record

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This paper cites Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools

Reference 1

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This paper cites Data pipeline training: Integrating automl to optimize the data flow of machine learning models,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Data pipeline training: Integrating automl to optimize the data flow of machine learning models,

Reference 2

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This paper cites Dream: Debugging and repairing automl pipelines,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Dream: Debugging and repairing automl pipelines,

Reference 3

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This paper cites Benchmark and survey of automated ma- chine learning frameworks,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Benchmark and survey of automated ma- chine learning frameworks,

Reference 4

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This paper cites Machine learning interpretability: A survey on methods and metrics,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Machine learning interpretability: A survey on methods and metrics,

Reference 5

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Observation 9a1e6da4-3984-498c-9c71-b926755ff04c · outbound

This paper cites Explainable Artificial Intelligence: a Systematic Review.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Explainable Artificial Intelligence: a Systematic Review

Reference 6

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Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Eight years of automl: categorisation, review and trends,

Reference 7

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Observation 1a5c2e85-5c18-4bb3-89a7-6e538421c04d · outbound

This paper cites AutoML: A systematic review on automated machine learning with neural architecture search,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance AutoML: A systematic review on automated machine learning with neural architecture search,

Reference 8

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Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance AutoML to date and beyond: Challenges and opportunities,

Reference 9

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Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Automated machine learning: past, present and future,

Reference 10

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Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Automated machine learning: A survey of tools and techniques,

Reference 11

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Observation 66af63d8-b33a-4820-9d0c-3f4e5b4ef87c · outbound

This paper cites MH-AutoML: Transparˆencia, interpretabilidade e desempenho na detecc ¸ ˜ao de malware android,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance MH-AutoML: Transparˆencia, interpretabilidade e desempenho na detecc ¸ ˜ao de malware android,

Reference 12

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This paper cites Hutter, L.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Hutter, L

Reference 13

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Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Survey on automated machine learning (automl) and meta learning,

Reference 14

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Observation f73dac37-58f4-45b3-9842-688bfc970e61 · outbound

This paper cites Data pre-processing pipeline generation for autoetl,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Data pre-processing pipeline generation for autoetl,

Reference 15

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This paper cites Auto-prep: Efficient and automated data preprocessing pipeline,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Auto-prep: Efficient and automated data preprocessing pipeline,

Reference 16

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Observation 7b5d9182-593a-4bb8-91de-d80742dfd6f1 · outbound

This paper cites Hyperparameter optimization for machine learning models based on bayesian optimization,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Hyperparameter optimization for machine learning models based on bayesian optimization,

Reference 17

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This paper cites On hyperparameter optimization of machine learning algorithms: Theory and practice,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance On hyperparameter optimization of machine learning algorithms: Theory and practice,

Reference 18

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Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance ” why should i trust you?

Reference 19

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This paper cites A unified approach to interpreting model predictions,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance A unified approach to interpreting model predictions,

Reference 20

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This paper cites Towards automated machine learning: Evaluation and comparison of AutoML approaches and tools,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Towards automated machine learning: Evaluation and comparison of AutoML approaches and tools,

Reference 21

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This paper cites An empirical evalu- ation of automated machine learning techniques for malware detection,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance An empirical evalu- ation of automated machine learning techniques for malware detection,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:09.478530Z

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This paper cites A comparison of AutoML tools for machine learning, deep learning and xgboost,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance A comparison of AutoML tools for machine learning, deep learning and xgboost,

Reference 23

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This paper cites Amlb: an automl benchmark,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Amlb: an automl benchmark,

Reference 24

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This paper cites Machine learning for all! benchmarking automated, explain- able, and coding-free platforms on civil and environmental engineering problems,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Machine learning for all! benchmarking automated, explain- able, and coding-free platforms on civil and environmental engineering problems,

Reference 25

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Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Automl for multi-label classification: Overview and empirical evaluation,

Reference 26

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Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Benchmarking automated machine learning (automl) frameworks for object detection,

Reference 27

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Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Bench- marking automl solutions for concrete strength prediction: Reliability, uncertainty, and dilemma,

Reference 28

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Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Comprehensive benchmarking analysis for evaluating effectiveness of transfer learning-based feature engineering in automl,

Reference 29

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Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Benchmark- ing automl clustering frameworks,

Reference 30

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Observation 0102d6d7-e266-49d4-b53f-414e4a58121f · outbound

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Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML

Reference 31

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Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL

Reference 32

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This paper cites AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data

Reference 33

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Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance H2O AutoML: Scalable automatic machine learning,

Reference 34

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Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Ludwig: a type-based declarative deep learning toolbox

Reference 35

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Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance NASirt: AutoML based learning with instance-level complexity information

Reference 36

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Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Alphaml: A clear, legible, explainable, transparent, and elucidative binary classification platform for tabular data,

Reference 37

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Observation 64b94825-cdab-4f7e-8d8b-a8cf19d307dd · outbound

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Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Integrated automl-based framework for optimizing shale gas production: A case study of the fuling shale gas field,

Reference 38

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Observation 3c19e165-d570-4f74-bc2d-8c5a70277e71 · outbound

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Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Automl framework for physical activities recognition,

Reference 39

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Observation 32cb32f3-cf6a-4c62-bf10-aa5be61f0154 · outbound

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Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Automl-gwl: automated machine learning model for the prediction of groundwater level,

Reference 40

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Observation e6a5e440-3323-4915-a91a-fb6c0d20225a · outbound

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Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Building domain-specific machine learning workflows: A conceptual framework for the state of the practice,

Reference 41

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Observation 572fdf8c-8abd-4e35-ac26-2509fb77d6e6 · outbound

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Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:03.264320Z

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Observation dc22d43d-0291-40c0-9f0f-e40e88daaa3a · outbound

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Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance AutoML for multi-class anomaly compensation of sensor drift,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:03.103763Z

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Observation 04f80e2e-91a1-4f2d-bc44-b08d84df53b9 · outbound

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Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Automl for deep recommender systems: A survey,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:02.912432Z

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Observation 6ac7acc4-95a1-4ae4-ad26-c10f2c5b85fd · outbound

This paper cites Xautoml: a visual analytics tool for understanding and validating automated machine learning,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Xautoml: a visual analytics tool for understanding and validating automated machine learning,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:02.717726Z

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Observation 4ff2e638-2f15-4c5c-991f-80cd22b29d69 · outbound

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Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Automated machine learning and explainable ai (automl-xai) for metabolomics: improving cancer diagnostics,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:02.539657Z

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Observation 90f255b9-7d62-40d7-a9bc-ebebb5c55666 · outbound

This paper cites Unlocking the black box: Towards interactive explainable automated machine learning,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Unlocking the black box: Towards interactive explainable automated machine learning,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:02.368038Z

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Observation 19f8708d-e492-403d-8985-b2a79859491f · outbound

This paper cites Automated machine learning with interpretation: a systematic review of methodologies and applications in healthcare,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Automated machine learning with interpretation: a systematic review of methodologies and applications in healthcare,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:02.032523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 81f48fb6-4d4f-4c9e-9820-767c72d5569a · outbound

This paper cites Automl to date and beyond: Challenges and opportunities,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Automl to date and beyond: Challenges and opportunities,

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T21:51:52.889055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55a5d55f-f460-444c-a247-5983f83c149e · outbound

This paper cites Two to trust: Automl for safe mod- elling and interpretable deep learning for robustness,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Two to trust: Automl for safe mod- elling and interpretable deep learning for robustness,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:01.776221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2b8a4a27-e9ea-47c2-a10d-123924f590c7 · outbound

This paper cites Model lineupper: Supporting interactive model comparison at multiple levels for automl,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Model lineupper: Supporting interactive model comparison at multiple levels for automl,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:01.512169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation dd732e59-0b9f-4187-9bee-a40cd5be9e73 · outbound

This paper cites Embracing diversity: Interpretable zero-shot classification beyond one vector per class,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Embracing diversity: Interpretable zero-shot classification beyond one vector per class,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:01.267495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5587c05e-40b5-465a-b2f3-7ac4b30b7b4d · outbound

This paper cites Towards trans- parent diabetes prediction: Combining automl and explainable ai for improved clinical insights,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Towards trans- parent diabetes prediction: Combining automl and explainable ai for improved clinical insights,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:01.075052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 85ffb263-bdf1-42d3-9706-a538d2955b7a · outbound

This paper cites An interpretable and generalizable machine learning model for predicting asthma out- comes: Integrating automl and explainable ai techniques,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance An interpretable and generalizable machine learning model for predicting asthma out- comes: Integrating automl and explainable ai techniques,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:00.859101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 51d20b0e-0736-48ff-8abb-5108e608aaed · outbound

This paper cites MH-AutoML,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance MH-AutoML,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:00.637882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 847efb8e-3d68-4c39-a087-d204f1c6435c · outbound

This paper cites Mh-fsf: um framework para reproduc ¸ ˜ao, experimentac ¸˜ao e avaliac ¸ ˜ao de m´etodos de selec ¸˜ao de caracter ´ısticas,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Mh-fsf: um framework para reproduc ¸ ˜ao, experimentac ¸˜ao e avaliac ¸ ˜ao de m´etodos de selec ¸˜ao de caracter ´ısticas,

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:00.425274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b9865a4e-55a3-43e8-80dc-69df16bb03d0 · outbound

This paper cites Efficient and robust automated machine learning,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Efficient and robust automated machine learning,

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:52:00.183385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 901f6dac-88ab-4046-92f4-5288c2caad52 · outbound

This paper cites Scaling tree-based automated machine learning to biomedical big data with a feature set selector,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Scaling tree-based automated machine learning to biomedical big data with a feature set selector,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:59.977681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation aefc6336-b742-4ab0-8bdd-5b33f3438d1b · outbound

This paper cites HyperGBM: A Full Pipeline AutoML Tool Integrated With Various GBM Models,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance HyperGBM: A Full Pipeline AutoML Tool Integrated With Various GBM Models,

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:59.809262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0b5687cf-6696-413d-bb25-6cf5987fa913 · outbound

This paper cites Auto-pytorch: multi-fidelity metalearning for efficient and robust autodl,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Auto-pytorch: multi-fidelity metalearning for efficient and robust autodl,

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:59.601369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a1a4a066-7c06-4eb8-9c61-a122fc86d846 · outbound

This paper cites LightAutoML: AutoML Solution for a Large Financial Services Ecosystem.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance LightAutoML: AutoML Solution for a Large Financial Services Ecosystem

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-06T21:51:54.180605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47e23cb3-b865-4f90-be1b-3c0b8ad3d596 · outbound

This paper cites Mljar: State-of-the-art automated machine learning framework for tabular data,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Mljar: State-of-the-art automated machine learning framework for tabular data,

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:59.418186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4084f506-8e64-480d-b699-f2619d17df2b · outbound

This paper cites Anonymized for review,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Anonymized for review,

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:59.037464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4eb869cb-31e9-4172-bdfa-fc0e5caabf5e · outbound

This paper cites AutoML: state of the art with a focus on anomaly detection, challenges, and research directions,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance AutoML: state of the art with a focus on anomaly detection, challenges, and research directions,

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:58.675241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 05deafc7-0d51-4968-bf32-0be331ceb538 · outbound

This paper cites Can fairness be automated? guidelines and opportunities for fairness-aware AutoML,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Can fairness be automated? guidelines and opportunities for fairness-aware AutoML,

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:58.430737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 54747d96-f9fa-4504-8438-ffe3f75903f5 · outbound

This paper cites Ex- plainable artificial intelligence (xai): Precepts, models, and opportunities for research in construction,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Ex- plainable artificial intelligence (xai): Precepts, models, and opportunities for research in construction,

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:58.279797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c96be467-3e81-429f-a640-ea524b07f254 · outbound

This paper cites Explainable artificial intelligence (xai): Concepts, taxonomies, oppor- tunities and challenges toward responsible ai,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Explainable artificial intelligence (xai): Concepts, taxonomies, oppor- tunities and challenges toward responsible ai,

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:58.138349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 6e48d04e-5a7a-4810-bc34-2f16a09eaa43 · outbound

This paper cites Review study of interpretation methods for future interpretable machine learning,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Review study of interpretation methods for future interpretable machine learning,

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:57.976530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 45df5eca-98c8-42d3-8dcd-a185bec73d30 · outbound

This paper cites Transparency of complex systems: The semantic transparency framework,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Transparency of complex systems: The semantic transparency framework,

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:57.765779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 48b86fb8-2a05-4a13-8697-a14f6ac35c5b · outbound

This paper cites Adroit: Android malware detection using meta-information,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Adroit: Android malware detection using meta-information,

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:57.528149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c5de0eba-d970-40e6-b919-0dd0ce872581 · outbound

This paper cites Androcrawl: studying alternative android marketplaces,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Androcrawl: studying alternative android marketplaces,

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:57.287853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 04bd0e99-341b-4aed-b5a0-d52cd62319a1 · outbound

This paper cites Android permission dataset,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Android permission dataset,

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:57.035363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4866220d-69ee-433d-b347-b9969d24a139 · outbound

This paper cites DefenseDroid: A Modern Approach to Android Malware Detection,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance DefenseDroid: A Modern Approach to Android Malware Detection,

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:56.813248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f234ca4e-39e0-47c9-983f-ab0ab21ae9ec · outbound

This paper cites Droidfusion: A novel multilevel classifier fusion approach for android malware detection,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Droidfusion: A novel multilevel classifier fusion approach for android malware detection,

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:56.604758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 989242a5-774a-4d83-a53a-049405a7a066 · outbound

This paper cites KronoDroid: Time-based Hybrid-featured Dataset for Effective Android Malware Detection and Characterization,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance KronoDroid: Time-based Hybrid-featured Dataset for Effective Android Malware Detection and Characterization,

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:56.345144Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bdcb460d-fafc-4b64-b2b9-3214840116fa · outbound

This paper cites Capturing the behavior of android malware with mh-100k: A novel and multidimensional dataset,.

Interpretable by Design: MH-AutoML for Transparent and Efficient Android Malware Detection without Compromising Performance Capturing the behavior of android malware with mh-100k: A novel and multidimensional dataset,

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T21:51:56.119339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers