# EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detection

arXiv: https://arxiv.org/abs/2110.03301 · HTML: https://mlsanity.com/paper/2110.03301/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 105 references that are in the map.
- First posted on arXiv 7 Oct 2021.

## Builds on (most cited first, 30 of 33)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [The Limitations of Deep Learning in Adversarial Settings](https://mlsanity.com/paper/1511.07528/) (2015)
- [On Evaluating Adversarial Robustness](https://mlsanity.com/paper/1902.06705/) (2019)
- [Malware Detection by Eating a Whole EXE](https://mlsanity.com/paper/1710.09435/) (2017)
- [TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time](https://mlsanity.com/paper/1807.07838/) (2018)
- [GenAttack: Practical Black-box Attacks with Gradient-Free Optimization](https://mlsanity.com/paper/1805.11090/) (2018)
- [Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN](https://mlsanity.com/paper/1702.05983/) (2017)
- [Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks](https://mlsanity.com/paper/2006.12834/) (2020)
- [Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables](https://mlsanity.com/paper/1803.04173/) (2018)
- [Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning](https://mlsanity.com/paper/1801.08917/) (2018)
- [Functionality-preserving Black-box Optimization of Adversarial Windows Malware](https://mlsanity.com/paper/2003.13526/) (2020)
- [Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection](https://mlsanity.com/paper/1704.08996/) (2017)
- [Security Evaluation of Pattern Classifiers under Attack](https://mlsanity.com/paper/1709.00609/) (2017)
- [Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware Detection](https://mlsanity.com/paper/2006.16545/) (2020)
- [Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries](https://mlsanity.com/paper/1901.03583/) (2019)
- [MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models](https://mlsanity.com/paper/1612.04433/) (2016)
- [Deceiving End-to-End Deep Learning Malware Detectors using Adversarial Examples](https://mlsanity.com/paper/1802.04528/) (2018)
- [Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection](https://mlsanity.com/paper/1808.04218/) (2018)
- [Black-Box Attacks against RNN based Malware Detection Algorithms](https://mlsanity.com/paper/1705.08131/) (2017)
- [Robust Android Malware Detection System against Adversarial Attacks using Q-Learning](https://mlsanity.com/paper/2101.12031/) (2021)
- [MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models (Extended Version)](https://mlsanity.com/paper/1711.07477/) (2017)
- [COPYCAT: Practical Adversarial Attacks on Visualization-Based Malware Detection](https://mlsanity.com/paper/1909.09735/) (2019)
- [MAB-Malware: A Reinforcement Learning Framework for Attacking Static Malware Classifiers](https://mlsanity.com/paper/2003.03100/) (2020)
- [Adversarial Samples on Android Malware Detection Systems for IoT Systems](https://mlsanity.com/paper/1902.04238/) (2019)
- [OFEI: A Semi-black-box Android Adversarial Sample Attack Framework Against DLaaS](https://mlsanity.com/paper/2105.11593/) (2021)
- [A Framework for Enhancing Deep Neural Networks Against Adversarial Malware](https://mlsanity.com/paper/2004.07919/) (2020)
- [Adversarial Detection of Flash Malware: Limitations and Open Issues](https://mlsanity.com/paper/1710.10225/) (2017)
- [Towards Accurate Labeling of Android Apps for Reliable Malware Detection](https://mlsanity.com/paper/2007.00464/) (2020)

## Built on by (most cited first, 13 of 13)

- [Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge Setting](https://mlsanity.com/paper/2309.01866/) (2023)
- [MalPurifier: Enhancing Android Malware Detection with Adversarial Purification against Evasion Attacks](https://mlsanity.com/paper/2312.06423/) (2023)
- [Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware Detection](https://mlsanity.com/paper/2205.15128/) (2022)
- [Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems](https://mlsanity.com/paper/2501.13782/) (2025)
- [On the Effectiveness of Adversarial Training on Malware Classifiers](https://mlsanity.com/paper/2412.18218/) (2024)
- [On the Security Risks of ML-based Malware Detection Systems: A Survey](https://mlsanity.com/paper/2505.10903/) (2025)
- [Adversarial Patterns: Building Robust Android Malware Classifiers](https://mlsanity.com/paper/2203.02121/) (2022)
- [Improving Adversarial Robustness in Android Malware Detection by Reducing the Impact of Spurious Correlations](https://mlsanity.com/paper/2408.16025/) (2024)
- [Decoding Android Malware with a Fraction of Features: An Attention-Enhanced MLP-SVM Approach](https://mlsanity.com/paper/2409.19234/) (2024)
- [Evaluating the robustness of adversarial defenses in malware detection systems](https://mlsanity.com/paper/2505.09342/) (2025)
- [LLM-Driven Feature-Level Adversarial Attacks on Android Malware Detectors](https://mlsanity.com/paper/2512.21404/) (2025)
- [Adversarial Vulnerability Under Temporal Concept Drift: A Longitudinal Study of Android Malware Detection](https://mlsanity.com/paper/2605.23623/) (2026)
- [REPLICANT: Learning Policies for Evading and Hardening Malware Detectors](https://mlsanity.com/paper/2608.28499/) (2026)

## Abstract (arXiv)

Over the last decade, researchers have extensively explored the vulnerabilities of Android malware detectors to adversarial examples through the development of evasion attacks; however, the practicality of these attacks in real-world scenarios remains arguable. The majority of studies have assumed attackers know the details of the target classifiers used for malware detection, while in reality, malicious actors have limited access to the target classifiers. This paper introduces EvadeDroid, a problem-space adversarial attack designed to effectively evade black-box Android malware detectors in real-world scenarios. EvadeDroid constructs a collection of problem-space transformations derived from benign donors that share opcode-level similarity with malware apps by leveraging an n-gram-based approach. These transformations are then used to morph malware instances into benign ones via an iterative and incremental manipulation strategy. The proposed manipulation technique is a query-efficient optimization algorithm that can find and inject optimal sequences of transformations into malware apps. Our empirical evaluations, carried out on 1K malware apps, demonstrate the effectiveness of our approach in generating real-world adversarial examples in both soft- and hard-label settings. Our findings reveal that EvadeDroid can effectively deceive diverse malware detectors that utilize different features with various feature types. Specifically, EvadeDroid achieves evasion rates of 80%-95% against DREBIN, Sec-SVM, ADE-MA, MaMaDroid, and Opcode-SVM with only 1-9 queries. Furthermore, we show that the proposed problem-space adversarial attack is able to preserve its stealthiness against five popular commercial antiviruses with an average of 79% evasion rate, thus demonstrating its feasibility in the real world.