Android Malware Detection Using Parallel Machine Learning Classifiers

TL;DR

This paper introduces a parallel machine learning approach combining diverse classifiers to improve early detection of Android malware, demonstrating enhanced accuracy and interpretability through empirical evaluation.

Contribution

It presents a novel composite classification model using parallel heterogeneous classifiers for Android malware detection, improving accuracy and interpretability.

Findings

Enhanced detection accuracy with combined classifiers

Faster white box analysis due to interpretability

Effective use of diverse classifiers in malware detection

Abstract

Mobile malware has continued to grow at an alarming rate despite on-going efforts towards mitigating the problem. This has been particularly noticeable on Android due to its being an open platform that has subsequently overtaken other platforms in the share of the mobile smart devices market. Hence, incentivizing a new wave of emerging Android malware sophisticated enough to evade most common detection methods. This paper proposes and investigates a parallel machine learning based classification approach for early detection of Android malware. Using real malware samples and benign applications, a composite classification model is developed from parallel combination of heterogeneous classifiers. The empirical evaluation of the model under different combination schemes demonstrates its efficacy and potential to improve detection accuracy. More importantly, by utilizing several classifiers with diverse characteristics, their strengths can be harnessed not only for enhanced Android malware detection but also quicker white box analysis by means of the more interpretable constituent classifiers.