# Adversarial Samples on Android Malware Detection Systems for IoT Systems

arXiv: https://arxiv.org/abs/1902.04238 · HTML: https://mlsanity.com/paper/1902.04238/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2019.

## Built on by (most cited first, 7 of 7)

- [Deep transfer learning for intrusion detection in industrial control networks: A comprehensive review](https://mlsanity.com/paper/2304.10550/) (2023)
- [EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detection](https://mlsanity.com/paper/2110.03301/) (2021)
- [Privacy Preservation in Artificial Intelligence and Extended Reality (AI-XR) Metaverses: A Survey](https://mlsanity.com/paper/2310.10665/) (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)
- ["How Does It Detect A Malicious App?" Explaining the Predictions of AI-based Android Malware Detector](https://mlsanity.com/paper/2111.05108/) (2021)
- [Adversarial Patterns: Building Robust Android Malware Classifiers](https://mlsanity.com/paper/2203.02121/) (2022)
- [PhilaeX: Explaining the Failure and Success of AI Models in Malware Detection](https://mlsanity.com/paper/2207.00740/) (2022)

## Abstract (arXiv)

Many IoT(Internet of Things) systems run Android systems or Android-like systems. With the continuous development of machine learning algorithms, the learning-based Android malware detection system for IoT devices has gradually increased. However, these learning-based detection models are often vulnerable to adversarial samples. An automated testing framework is needed to help these learning-based malware detection systems for IoT devices perform security analysis. The current methods of generating adversarial samples mostly require training parameters of models and most of the methods are aimed at image data. To solve this problem, we propose a testing framework for learning-based Android malware detection systems(TLAMD) for IoT Devices. The key challenge is how to construct a suitable fitness function to generate an effective adversarial sample without affecting the features of the application. By introducing genetic algorithms and some technical improvements, our test framework can generate adversarial samples for the IoT Android Application with a success rate of nearly 100% and can perform black-box testing on the system.