High Accuracy Android Malware Detection Using Ensemble Learning

TL;DR

This paper presents an ensemble learning-based approach for Android malware detection that combines static analysis with machine learning to achieve high accuracy and low false positives.

Contribution

It introduces a novel ensemble learning method leveraging static analysis features for improved Android malware detection accuracy.

Findings

Achieves 97.3 to 99% detection accuracy

Utilizes a large feature space for ensemble learning

Maintains very low false positive rates

Abstract

With over 50 billion downloads and more than 1.3 million apps in the Google official market, Android has continued to gain popularity amongst smartphone users worldwide. At the same time there has been a rise in malware targeting the platform, with more recent strains employing highly sophisticated detection avoidance techniques. As traditional signature based methods become less potent in detecting unknown malware, alternatives are needed for timely zero-day discovery. Thus this paper proposes an approach that utilizes ensemble learning for Android malware detection. It combines advantages of static analysis with the efficiency and performance of ensemble machine learning to improve Android malware detection accuracy. The machine learning models are built using a large repository of malware samples and benign apps from a leading antivirus vendor. Experimental results and analysis presented shows that the proposed method which uses a large feature space to leverage the power of ensemble learning is capable of 97.3 to 99 percent detection accuracy with very low false positive rates.