PRO-Net: A Novel Framework for Augmenting Android Security against Botnets and Malware through Advanced Detection Metrics

Iqra Pervaiz; Rutaba Irfan; Mujeeb-Ur-Rehman; Zoraiz Ali

Author(s)

Iqra Pervaiz Department of Computer Science, University of Management and Technology, Sialkot 51310, Pakistan

Rutaba Irfan Department of Computer Science, University of Management and Technology, Sialkot 51310, Pakistan

Mujeeb-Ur-Rehman Department of Computer Science, University of Management and Technology, Sialkot 51310, Pakistan

Zoraiz Ali Department of Computer Science, University of Management and Technology, Sialkot 51310, Pakistan

Abstract

Android is a popular smartphone operating system, which dominates the market with a global share of approximately 70.29%. Over 255 billion applications are available on the official Play Store, with many more available from other sources. Android is the leading platform for smartphone applications, with an increase in the number of applications available. However, the demand for the Android operating systems has also attracted the attention of malicious software developers. A growing number of attackers are targeting mobile devices, converting them into bots for their operations. This enables cybercriminals to gain control of compromised devices, establishing networks known as botnets. These botnets are then utilized to execute harmful activities such as Distributed Denial-of-Service (DDoS) attacks, stealing sensitive data and spamming. Unfortunately, some malicious apps are designed specifically for Android systems to perform different types of offenses, such as worms, exploits, trojans, rootkit viruses etc. These applications are often delivered in various versions to target a larger audience, making them difficult to detect. As the safety of the Android operating system is crucial, Machine Learning (ML) and Deep Learning (DL) algorithms alone are not enough. Therefore, a new PRO-Net system has been devised to protect against data breaches. The proposed framework, PRO-NET, is evaluated using precision, accuracy, and F1 score metrics. The study reveals that the system provides symmetry between apps and malware, which is essential for maintaining the security of the Android operating system.

Publication Details

Page(s) 148-161

DOI DOI not available

Published Journal: The Sciencetech, Volume: 5, Issue: 4, Year: 2024

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Keywords

Deep learning, Machine learning algorithms, Malware Detection, Static Malware Analysis, Android Application

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