# DL-Droid: Deep learning based android malware detection using real devices

arXiv: https://arxiv.org/abs/1911.10113 · HTML: https://mlsanity.com/paper/1911.10113/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2019.

## Built on by (most cited first, 11 of 11)

- [Deep Learning for Android Malware Defenses: a Systematic Literature Review](https://mlsanity.com/paper/2103.05292/) (2021)
- [Unrestricted Black-box Adversarial Attack Using GAN with Limited Queries](https://mlsanity.com/paper/2208.11613/) (2022)
- [DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection based on Image Representation of Bytecode](https://mlsanity.com/paper/2109.03326/) (2021)
- [A New Deep Boosted CNN and Ensemble Learning based IoT Malware Detection](https://mlsanity.com/paper/2212.08008/) (2022)
- [MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks](https://mlsanity.com/paper/2312.06423/) (2023)
- [Deep Multi-Task Learning for Malware Image Classification](https://mlsanity.com/paper/2405.05906/) (2024)
- [Dynamic detection of mobile malware using smartphone data and machine learning](https://mlsanity.com/paper/2107.11167/) (2021)
- [On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunities](https://mlsanity.com/paper/2306.07118/) (2023)
- [Collective Intelligence: Decentralized Learning for Android Malware Detection in IoT with Blockchain](https://mlsanity.com/paper/2102.13376/) (2021)
- [DeepTrust: Multi-Step Classification through Dissimilar Adversarial Representations for Robust Android Malware Detection](https://mlsanity.com/paper/2510.12310/) (2025)
- [Better Call Graphs: A New Dataset of Function Call Graphs for Malware Classification](https://mlsanity.com/paper/2512.20872/) (2025)

## Abstract (arXiv)

The Android operating system has been the most popular for smartphones and tablets since 2012. This popularity has led to a rapid raise of Android malware in recent years. The sophistication of Android malware obfuscation and detection avoidance methods have significantly improved, making many traditional malware detection methods obsolete. In this paper, we propose DL-Droid, a deep learning system to detect malicious Android applications through dynamic analysis using stateful input generation. Experiments performed with over 30,000 applications (benign and malware) on real devices are presented. Furthermore, experiments were also conducted to compare the detection performance and code coverage of the stateful input generation method with the commonly used stateless approach using the deep learning system. Our study reveals that DL-Droid can achieve up to 97.8% detection rate (with dynamic features only) and 99.6% detection rate (with dynamic + static features) respectively which outperforms traditional machine learning techniques. Furthermore, the results highlight the significance of enhanced input generation for dynamic analysis as DL-Droid with the state-based input generation is shown to outperform the existing state-of-the-art approaches.