# Robust Android Malware Detection System against Adversarial Attacks using Q-Learning

arXiv: https://arxiv.org/abs/2101.12031 · HTML: https://mlsanity.com/paper/2101.12031/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 44 references that are in the map.
- First posted on arXiv 27 Jan 2021.

## Builds on (most cited first, 15 of 15)

- [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)
- [Adversarial Machine Learning at Scale](https://mlsanity.com/paper/1611.01236/) (2016)
- [Ensemble Adversarial Training: Attacks and Defenses](https://mlsanity.com/paper/1705.07204/) (2017)
- [The Limitations of Deep Learning in Adversarial Settings](https://mlsanity.com/paper/1511.07528/) (2015)
- [Evasion Attacks against Machine Learning at Test Time](https://mlsanity.com/paper/1708.06131/) (2017)
- [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018)
- [Label Sanitization against Label Flipping Poisoning Attacks](https://mlsanity.com/paper/1803.00992/) (2018)
- [Automated Poisoning Attacks and Defenses in Malware Detection Systems: An Adversarial Machine Learning Approach](https://mlsanity.com/paper/1706.04146/) (2017)
- [Adversarial Examples - A Complete Characterisation of the Phenomenon](https://mlsanity.com/paper/1810.01185/) (2018)
- [Malware Detection using Machine Learning and Deep Learning](https://mlsanity.com/paper/1904.02441/) (2019)
- [Evolution and Detection of Polymorphic and Metamorphic Malwares: A Survey](https://mlsanity.com/paper/1406.7061/) (2014)
- [A New Android Malware Detection Approach Using Bayesian Classification](https://mlsanity.com/paper/1608.00848/) (2016)
- [Comparison of Deep Learning and the Classical Machine Learning Algorithm for the Malware Detection](https://mlsanity.com/paper/1809.05889/) (2018)
- [Android Malicious Application Classification Using Clustering](https://mlsanity.com/paper/1904.10142/) (2019)

## Built on by (most cited first, 12 of 12)

- [Deep Learning for Android Malware Defenses: a Systematic Literature Review](https://mlsanity.com/paper/2103.05292/) (2021)
- [EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detection](https://mlsanity.com/paper/2110.03301/) (2021)
- [Deep Reinforcement Learning for Cybersecurity Threat Detection and Protection: A Review](https://mlsanity.com/paper/2206.02733/) (2022)
- [Mal2GCN: A Robust Malware Detection Approach Using Deep Graph Convolutional Networks With Non-Negative Weights](https://mlsanity.com/paper/2108.12473/) (2021)
- [Level Up with ML Vulnerability Identification: Leveraging Domain Constraints in Feature Space for Robust Android Malware Detection](https://mlsanity.com/paper/2205.15128/) (2022)
- [LSTM Hyper-Parameter Selection for Malware Detection: Interaction Effects and Hierarchical Selection Approach](https://mlsanity.com/paper/2109.11500/) (2021)
- [ADVERSARIALuscator: An Adversarial-DRL Based Obfuscator and Metamorphic Malware SwarmGenerator](https://mlsanity.com/paper/2109.11542/) (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)
- [Deep Learning Based XIoT Malware Analysis: A Comprehensive Survey, Taxonomy, and Research Challenges](https://mlsanity.com/paper/2410.13894/) (2024)
- [On the Effectiveness of Adversarial Training on Malware Classifiers](https://mlsanity.com/paper/2412.18218/) (2024)
- [DRo: A data-scarce mechanism to revolutionize the performance of Deep Learning based Security Systems](https://mlsanity.com/paper/2109.05470/) (2021)
- [Improving Adversarial Robustness in Android Malware Detection by Reducing the Impact of Spurious Correlations](https://mlsanity.com/paper/2408.16025/) (2024)

## Abstract (arXiv)

The current state-of-the-art Android malware detection systems are based on machine learning and deep learning models. Despite having superior performance, these models are susceptible to adversarial attacks. Therefore in this paper, we developed eight Android malware detection models based on machine learning and deep neural network and investigated their robustness against adversarial attacks. For this purpose, we created new variants of malware using Reinforcement Learning, which will be misclassified as benign by the existing Android malware detection models. We propose two novel attack strategies, namely single policy attack and multiple policy attack using reinforcement learning for white-box and grey-box scenario respectively. Putting ourselves in the adversary's shoes, we designed adversarial attacks on the detection models with the goal of maximizing fooling rate, while making minimum modifications to the Android application and ensuring that the app's functionality and behavior do not change. We achieved an average fooling rate of 44.21% and 53.20% across all the eight detection models with a maximum of five modifications using a single policy attack and multiple policy attack, respectively. The highest fooling rate of 86.09% with five changes was attained against the decision tree-based model using the multiple policy approach. Finally, we propose an adversarial defense strategy that reduces the average fooling rate by threefold to 15.22% against a single policy attack, thereby increasing the robustness of the detection models i.e. the proposed model can effectively detect variants (metamorphic) of malware. The experimental analysis shows that our proposed Android malware detection system using reinforcement learning is more robust against adversarial attacks.