Monet: A User-oriented Behavior-based Malware Variants Detection System for Android
Mingshen Sun, Xiaolei Li, John C.S. Lui, Richard T.B. Ma, Zhenkai, Liang

TL;DR
MONET is a malware detection system for Android that combines runtime behavior analysis with static structures, achieving high accuracy in identifying malware variants and resisting obfuscation techniques with minimal performance impact.
Contribution
This paper introduces MONET, a novel framework that integrates behavior-based and static analysis for effective malware variant detection on Android devices.
Findings
Achieves 99% accuracy in malware variant detection
Resists 10 different obfuscation and transformation techniques
Maintains around 7% performance overhead and 3% battery overhead
Abstract
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…
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Taxonomy
TopicsAdvanced Malware Detection Techniques · Network Security and Intrusion Detection · Mobile and Web Applications
