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A Scalable Malware Detection Approach through Significant Permission Identification for Android Devices - Volume -2 | Issue - 1 | 2024, (JAN-MAR)


Category: Engineering & Technology

Published Date: 15-May-2024

Graphical Abstract

Praveen Kumar Karri, D.Jaya Kumari, Sowmya Sree Karri


Smartphones, Malicious Software, Android, Machine Learning, Scalable Detection, Mobile security

The global ubiquity of smartphones has led to the availability of many free apps for gaming, communication, financial, and educational needs. However, hazardous malicious software targeting smartphones has increased as the global adoption of these devices has grown. Malware is growing rapidly, with reports predicting a new Android app every 10 seconds, threatening the mobile ecosystem. Due to Android's versatility, users can install apps from third-party app shops and file-sharing websites, compounding malware outbreaks. The seriousness of this situation requires scalable malware detection. Based on permission usage analysis, this project introduces Significant Permission Identification (SigPID), a novel malware detection technique. SigPID uses a three-tiered permission pruning mechanism to discover the most important permissions for distinguishing benign from malicious apps, unlike standard methods that scan all Android permissions. The system first uses the Random Forest method for machine learning classifications. The study reduces non-sensitive permissions by identifying benign and harmful permission lists. Support Vector Machine classification, K-Nearest Neighbor, and Linear Regression are then used to a fresh dataset. SigPID, written in Python 3.7, is a powerful and scalable Android malware countermeasure. SigPID uses advanced machine learning and large permissions to protect the mobile ecosystem from harmful apps, making it safer and more secure.

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