PAIRED: An Explainable Lightweight Android Malware Detection System

• With approximately 2 billion active devices, the Android operating system tops all other operating systems in terms of the number of devices using it. Android has gained wide popularity not only as a smartphone operating system, but also as an operating system for vehicles, tablets, smart appliances, and Internet of Things devices. Consequently, security challenges have arisen with the rapid adoption of the Android operating system. Thousands of malicious applications have been created and are being downloaded by unsuspecting users. This paper presents a lightweight Android malware detection system based on explainable machine learning. The proposed system uses the features extracted from applications to identify malicious and benign malware. The proposed system is tested, showing an accuracy exceeding 98% while maintaining its small footprint on the device. In addition, the classifier model is explained using Shapley Additive Explanation (SHAP) values.

Subject headings

NATURVETENSKAP  -- Data- och informationsvetenskap -- Systemvetenskap, informationssystem och informatik (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Information Systems (hsv//eng)

Keyword

Android

malware

malware detection

XAI

machine learning

Information systems

Informationssystem

Publication and Content Type

ref (subject category)

art (subject category)