A Review on The Use of Deep Learning in Android Malware Detection

TL;DR

This paper reviews the application of deep learning techniques in Android malware detection, analyzing static, dynamic, and hybrid methods to identify progress, challenges, and future research directions.

Contribution

It provides an extensive survey of deep learning-based Android malware detection methods, highlighting their key concepts, contributions, and limitations.

Findings

Deep learning methods have been effectively applied in static, dynamic, and hybrid analysis for malware detection.

Current approaches face challenges related to dataset quality, model interpretability, and evolving malware tactics.

The survey identifies gaps and suggests future research directions in deep learning for Android security.

Abstract

Android is the predominant mobile operating system for the past few years. The prevalence of devices that can be powered by Android magnetized not merely application developers but also malware developers with criminal intention to design and spread malicious applications that can affect the normal work of Android phones and tablets, steal personal information and credential data, or even worse lock the phone and ask for ransom. Researchers persistently devise countermeasures strategies to fight back malware. One of these strategies applied in the past five years is the use of deep learning methods in Android malware detection. This necessitates a review to inspect the accomplished work in order to know where the endeavors have been established, identify unresolved problems, and motivate future research directions. In this work, an extensive survey of static analysis, dynamic analysis, and hybrid analysis that utilized deep learning methods are reviewed with an elaborated discussion on their key concepts, contributions, and limitations.