Deep Learning for Android Malware Defenses: a Systematic Literature Review

Abstract

Malicious applications (particularly those targeting the Android platform) pose a serious threat to developers and end-users. Numerous research efforts have been devoted to developing effective approaches to defend against Android malware. However, given the explosive growth of Android malware and the continuous advancement of malicious evasion technologies like obfuscation and reflection, Android malware defense approaches based on manual rules or traditional machine learning may not be effective. In recent years, a dominant research field called deep learning (DL), which provides a powerful feature abstraction ability, has demonstrated a compelling and promising performance in a variety of areas, like natural language processing and computer vision. To this end, employing deep learning techniques to thwart Android malware attacks has recently garnered considerable research attention. Yet, no systematic literature review focusing on deep learning approaches for Android Malware defenses exists. In this paper, we conducted a systematic literature review to search and analyze how deep learning approaches have been applied in the context of malware defenses in the Android environment. As a result, a total of 132 studies covering the period 2014-2021 were identified. Our investigation reveals that, while the majority of these sources mainly consider DL-based on Android malware detection, 53 primary studies (40.1 percent) design defense approaches based on other scenarios. This review also discusses research trends, research focuses, challenges, and future research directions in DL-based Android malware defenses.

Accepted by ACM Computing Surveys

References in corpus (16)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Deep Learning in Neural Networks: An Overview
• Semi-Supervised Classification with Graph Convolutional Networks
• Distributed Representations of Sentences and Documents
• A Brief Survey of Deep Reinforcement Learning
• DL-Droid: Deep learning based android malware detection using real devices
• Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN
• Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware Detection
• Predicting Defective Lines Using a Model-Agnostic Technique
• Deep Learning for Android Malware Defenses: a Systematic Literature Review
• Robust Android Malware Detection System against Adversarial Attacks using Q-Learning
• EMULATOR vs REAL PHONE: Android Malware Detection Using Machine Learning
• A Review on The Use of Deep Learning in Android Malware Detection
• MadDroid: Characterising and Detecting Devious Ad Content for Android Apps
• Can We Leverage Predictive Uncertainty to Detect Dataset Shift and Adversarial Examples in Android Malware Detection?
• Explainable AI for Software Engineering

Cited by in corpus (7)

• A Survey on Large Language Models for Code Generation
• Trustworthy Graph Neural Networks: Aspects, Methods and Trends
• Deep Learning for Android Malware Defenses: a Systematic Literature Review
• PAD: Towards Principled Adversarial Malware Detection Against Evasion Attacks
• Automatically Detecting API-induced Compatibility Issues in Android Apps: A Comparative Analysis (Replicability Study)
• A survey on hardware-based malware detection approaches
• Towards Using Data-Influence Methods to Detect Noisy Samples in Source Code Corpora