ShielDroid: A Hybrid Approach Integrating Machine and Deep Learning for Android Malware Detection

2026-08-04

AI Summary

This study addresses the limitations of existing static analysis techniques in effectively detecting sophisticated Android malware that activates only at runtime, enabling it to evade app store vetting and compromise user devices. To overcome this challenge, the authors propose a behavior-based real-time detection framework that integrates dynamic analysis with a novel hybrid machine learning model combining Random Forest and Multilayer Perceptron architectures. This approach achieves a balance between high detection accuracy and low computational latency. Experimental results demonstrate that the proposed method attains a 97.5% detection accuracy within just 22.945 seconds, significantly enhancing the capability of mobile platforms to provide real-time defense against advanced malware threats.

Abstract

The rapid advancement of modern technology has led to a significant increase in the use of smart devices, such as smartphones and tablets, resulting in the widespread adoption of mobile applications. Although applications are required to undergo malware screening before being published on official app stores, many malicious applications successfully evade detection by concealing sophisticated malware variants. These malicious behaviors are often activated only during runtime, making them difficult to identify through conventional static analysis. As a result, malware may remain undetected until after installation, potentially causing irreversible damage to users and their devices. This study presents a real-time Android malware detection framework that analyzes application behavior to accurately identify and classify complex malware. The proposed approach employs a hybrid dynamic analysis technique to distinguish malicious applications from benign ones. After preprocessing and filtering the collected dataset, the applications are classified using multiple machine learning algorithms. A comprehensive performance evaluation is conducted to compare the effectiveness of different classification techniques in terms of detection accuracy and execution time. Experimental results demonstrate that a hybrid model combining Random Forest and a Multilayer Perceptron achieves the best overall performance, attaining an accuracy of 97.5% with an execution time of 22.945 seconds. The proposed framework can enhance mobile device security by enabling timely detection of malicious applications and reducing the risk of cyberattacks.

Problem

• Android malware detection
• runtime behavior
• evasion techniques
• mobile security
• malicious applications

Innovation

• hybrid machine learning
• dynamic analysis
• Android malware detection
• Random Forest
• Multilayer Perceptron