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

arXiv: https://arxiv.org/abs/2506.23314 · HTML: https://mlsanity.com/paper/2506.23314/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 73 references that are in the map.
- First posted on arXiv 29 Jun 2025.

## Builds on (most cited first, 24 of 24)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data](https://mlsanity.com/paper/2003.06505/) (2020)
- [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://mlsanity.com/paper/2007.15745/) (2020)
- [AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML](https://mlsanity.com/paper/2410.02958/) (2024)
- [Explainable Artificial Intelligence: a Systematic Review](https://mlsanity.com/paper/2006.00093/) (2020)
- [Auto-PyTorch Tabular: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL](https://mlsanity.com/paper/2006.13799/) (2020)
- [AutoML to Date and Beyond: Challenges and Opportunities](https://mlsanity.com/paper/2010.10777/) (2020)
- [AMLB: an AutoML Benchmark](https://mlsanity.com/paper/2207.12560/) (2022)
- [LightAutoML: AutoML Solution for a Large Financial Services Ecosystem](https://mlsanity.com/paper/2109.01528/) (2021)
- [AutoML for Deep Recommender Systems: A Survey](https://mlsanity.com/paper/2203.13922/) (2022)
- [Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools](https://mlsanity.com/paper/1908.05557/) (2019)
- [Ludwig: a type-based declarative deep learning toolbox](https://mlsanity.com/paper/1909.07930/) (2019)
- [Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoML](https://mlsanity.com/paper/2303.08485/) (2023)
- [Model LineUpper: Supporting Interactive Model Comparison at Multiple Levels for AutoML](https://mlsanity.com/paper/2104.04375/) (2021)
- [XAutoML: A Visual Analytics Tool for Understanding and Validating Automated Machine Learning](https://mlsanity.com/paper/2202.11954/) (2022)
- [Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State-of-the-Practice](https://mlsanity.com/paper/2203.08638/) (2022)
- [Data Pipeline Training: Integrating AutoML to Optimize the Data Flow of Machine Learning Models](https://mlsanity.com/paper/2402.12916/) (2024)
- [Explainable Artificial Intelligence: Precepts, Methods, and Opportunities for Research in Construction](https://mlsanity.com/paper/2211.06579/) (2022)
- [Embracing Diversity: Interpretable Zero-shot classification beyond one vector per class](https://mlsanity.com/paper/2404.16717/) (2024)
- [AutoML for Multi-Class Anomaly Compensation of Sensor Drift](https://mlsanity.com/paper/2502.19180/) (2025)
- [NASirt: AutoML based learning with instance-level complexity information](https://mlsanity.com/paper/2008.11846/) (2020)
- [DREAM: Debugging and Repairing AutoML Pipelines](https://mlsanity.com/paper/2401.00379/) (2023)

## Abstract (arXiv)

Malware detection in Android systems requires both cybersecurity expertise and machine learning (ML) techniques. Automated Machine Learning (AutoML) has emerged as an approach to simplify ML development by reducing the need for specialized knowledge. However, current AutoML solutions typically operate as black-box systems with limited transparency, interpretability, and experiment traceability. To address these limitations, we present MH-AutoML, a domain-specific framework for Android malware detection. MH-AutoML automates the entire ML pipeline, including data preprocessing, feature engineering, algorithm selection, and hyperparameter tuning. The framework incorporates capabilities for interpretability, debugging, and experiment tracking that are often missing in general-purpose solutions. In this study, we compare MH-AutoML against seven established AutoML frameworks: Auto-Sklearn, AutoGluon, TPOT, HyperGBM, Auto-PyTorch, LightAutoML, and MLJAR. Results show that MH-AutoML achieves better recall rates while providing more transparency and control. The framework maintains computational efficiency comparable to other solutions, making it suitable for cybersecurity applications where both performance and explainability matter.