MADAM: Effective and Efficient Behavior-based Android Malware Detection and Prevention

Abstract

Android malware poses a significant threat, demanding robust and efficient detection mechanisms. This research proposes MADAM (Malware Detection and Mitigation), a novel behavior-based system leveraging machine learning to identify malicious applications. MADAM analyzes application runtime behavior, extracting features to train a highly accurate classification model. Our results demonstrate significantly improved detection rates and reduced false positives compared to existing signature-based approaches. The system is designed for efficiency, minimizing resource consumption on Android devices. This work contributes to enhanced Android security by providing a proactive and adaptive defense against evolving malware threats.

Introduction

The Android operating system's open nature and vast app market make it a prime target for malware. Traditional signature-based detection methods struggle to keep pace with the rapid evolution of malware variants, exhibiting high false negative rates. Behavior-based approaches, analyzing application runtime actions, offer a more robust alternative. However, challenges remain in efficiently extracting relevant behavioral features and developing effective classification models that minimize resource overhead on mobile devices. This research addresses these challenges by proposing a lightweight and efficient behavior-based system capable of real-time malware detection and prevention.

Objectives

• Develop an efficient behavior-based Android malware detection system.
• Achieve high accuracy and low false positive rates in malware detection.
• Minimize resource consumption on the Android device.

Technical Details

• Android Studio with Java/Kotlin
• Behavioral monitoring tools (API access, system logs)
• Machine learning models for classification (e.g., Random Forest, SVM)
• Feature extraction from runtime behavior (CPU usage, permissions, network access, etc.)
• Lightweight detection algorithm optimized for mobile devices