DroidDetector: Android Malware Characterization and Detection Using Deep Learning

Abstract

The proliferation of Android malware necessitates robust and efficient detection mechanisms. This research proposes DroidDetector, a deep learning-based system for characterizing and detecting Android malware. DroidDetector utilizes a novel approach combining static and dynamic analysis features extracted from Android applications to train a deep neural network for accurate malware classification. Our results demonstrate superior detection accuracy and improved characterization capabilities compared to existing methods, offering a significant advancement in Android security. The system efficiently handles large datasets and adapts to evolving malware techniques, providing a more robust and scalable solution for Android malware detection.

Introduction

Android's open-source nature and widespread use make it a prime target for malware developers. Traditional signature-based detection methods struggle to keep pace with the rapid evolution of new malware variants. Machine learning, particularly deep learning, offers a promising alternative by learning complex patterns from large datasets of benign and malicious apps. However, challenges remain in effectively extracting relevant features from diverse Android applications and building robust, generalizable models that can handle the constantly evolving landscape of malware. This research addresses these challenges by developing a sophisticated deep learning model capable of high-accuracy detection and detailed malware characterization.

Objectives

• Develop a deep learning model for accurate detection of Android malware.
• Characterize detected malware based on their functionalities and behaviors.
• Evaluate the performance of the proposed system against state-of-the-art malware detection methods.

Technical Details

• Static and dynamic analysis of APK files
• Deep Neural Networks for classification (TensorFlow/Keras)
• Dataset: Drebin, CICAndMal, or custom APK dataset
• Feature engineering using permissions, API calls, and runtime behaviors
• Tech Stack: Python, Android Emulator, TensorFlow, ADB tools