# A Performance-Sensitive Malware Detection System Using Deep Learning on Mobile Devices

arXiv: https://arxiv.org/abs/2005.04970 · HTML: https://mlsanity.com/paper/2005.04970/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2020.

## Built on by (most cited first, 9 of 9)

- [MOVE: Effective and Harmless Ownership Verification via Embedded External Features](https://mlsanity.com/paper/2208.02820/) (2022)
- [Deep Learning for Android Malware Defenses: a Systematic Literature Review](https://mlsanity.com/paper/2103.05292/) (2021)
- [Pitfalls in Language Models for Code Intelligence: A Taxonomy and Survey](https://mlsanity.com/paper/2310.17903/) (2023)
- [Unraveling the Key of Machine Learning-based Android Malware Detection](https://mlsanity.com/paper/2402.02953/) (2024)
- [INK: Inheritable Natural Backdoor Attack Against Model Distillation](https://mlsanity.com/paper/2304.10985/) (2023)
- [A Survey on the Application of Generative Adversarial Networks in Cybersecurity: Prospective, Direction and Open Research Scopes](https://mlsanity.com/paper/2407.08839/) (2024)
- [SeqNet: An Efficient Neural Network for Automatic Malware Detection](https://mlsanity.com/paper/2205.03850/) (2022)
- [On the Security Risks of ML-based Malware Detection Systems: A Survey](https://mlsanity.com/paper/2505.10903/) (2025)
- [Collective Intelligence: Decentralized Learning for Android Malware Detection in IoT with Blockchain](https://mlsanity.com/paper/2102.13376/) (2021)

## Abstract (arXiv)

Currently, Android malware detection is mostly performed on server side against the increasing number of malware. Powerful computing resource provides more exhaustive protection for app markets than maintaining detection by a single user. However, apart from the applications provided by the official market, apps from unofficial markets and third-party resources are always causing serious security threats to end-users. Meanwhile, it is a time-consuming task if the app is downloaded first and then uploaded to the server side for detection, because the network transmission has a lot of overhead. In addition, the uploading process also suffers from the security threats of attackers. Consequently, a last line of defense on mobile devices is necessary and much-needed. In this paper, we propose an effective Android malware detection system, MobiTive, leveraging customized deep neural networks to provide a real-time and responsive detection environment on mobile devices. MobiTive is a preinstalled solution rather than an app scanning and monitoring engine using after installation, which is more practical and secure. Original deep learning models cannot be directly deployed and executed on mobile devices due to various performance limitations, such as computation power, memory size, and energy. Therefore, we evaluate and investigate the following key points:(1) the performance of different feature extraction methods based on source code or binary code;(2) the performance of different feature type selections for deep learning on mobile devices;(3) the detection accuracy of different deep neural networks on mobile devices;(4) the real-time detection performance and accuracy on different mobile devices;(5) the potential based on the evolution trend of mobile devices' specifications; and finally we further propose a practical solution (MobiTive) to detect Android malware on mobile devices.