# Monet: A User-oriented Behavior-based Malware Variants Detection System for Android

arXiv: https://arxiv.org/abs/1612.03312 · HTML: https://mlsanity.com/paper/1612.03312/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2016.

## Built on by (most cited first, 4 of 4)

- [MG-DVD: A Real-time Framework for Malware Variant Detection Based on Dynamic Heterogeneous Graph Learning](https://mlsanity.com/paper/2106.12288/) (2021)
- [Collective Intelligence: Decentralized Learning for Android Malware Detection in IoT with Blockchain](https://mlsanity.com/paper/2102.13376/) (2021)
- [Brief View and Analysis to Latest Android Security Issues and Approaches](https://mlsanity.com/paper/2109.00805/) (2021)
- [Mask Off: Analytic-based Malware Detection By Transfer Learning and Model Personalization](https://mlsanity.com/paper/2211.10843/) (2022)

## Abstract (arXiv)

Android, the most popular mobile OS, has around 78% of the mobile market share. Due to its popularity, it attracts many malware attacks. In fact, people have discovered around one million new malware samples per quarter, and it was reported that over 98% of these new malware samples are in fact "derivatives" (or variants) from existing malware families. In this paper, we first show that runtime behaviors of malware's core functionalities are in fact similar within a malware family. Hence, we propose a framework to combine "runtime behavior" with "static structures" to detect malware variants. We present the design and implementation of MONET, which has a client and a backend server module. The client module is a lightweight, in-device app for behavior monitoring and signature generation, and we realize this using two novel interception techniques. The backend server is responsible for large scale malware detection. We collect 3723 malware samples and top 500 benign apps to carry out extensive experiments of detecting malware variants and defending against malware transformation. Our experiments show that MONET can achieve around 99% accuracy in detecting malware variants. Furthermore, it can defend against 10 different obfuscation and transformation techniques, while only incurs around 7% performance overhead and about 3% battery overhead. More importantly, MONET will automatically alert users with intrusion details so to prevent further malicious behaviors.