Android Malware Family Classification Based on Resource Consumption over Time

TL;DR

This paper presents a dynamic analysis method for Android malware family classification using resource consumption metrics, achieving 82% accuracy with a reproducible approach based on publicly available tools.

Contribution

The authors introduce a resource consumption-based classification method that is easier to reproduce and can be applied to physical devices without modifying the Android OS.

Findings

Achieved 82% classification accuracy on Drebin dataset.

Method is reproducible using publicly available tools.

Effective against malware that evades static analysis.

Abstract

The vast majority of today's mobile malware targets Android devices. This has pushed the research effort in Android malware analysis in the last years. An important task of malware analysis is the classification of malware samples into known families. Static malware analysis is known to fall short against techniques that change static characteristics of the malware (e.g. code obfuscation), while dynamic analysis has proven effective against such techniques. To the best of our knowledge, the most notable work on Android malware family classification purely based on dynamic analysis is DroidScribe. With respect to DroidScribe, our approach is easier to reproduce. Our methodology only employs publicly available tools, does not require any modification to the emulated environment or Android OS, and can collect data from physical devices. The latter is a key factor, since modern mobile malware can detect the emulated environment and hide their malicious behavior. Our approach relies on resource consumption metrics available from the proc file system. Features are extracted through detrended fluctuation analysis and correlation. Finally, a SVM is employed to classify malware into families. We provide an experimental evaluation on malware samples from the Drebin dataset, where we obtain a classification accuracy of 82%, proving that our methodology achieves an accuracy comparable to that of DroidScribe. Furthermore, we make the software we developed publicly available, to ease the reproducibility of our results.