Android Malware Detection using Machine Learning and Convolutional Neural Network

Parthiban, Sai Hari (2024) Android Malware Detection using Machine Learning and Convolutional Neural Network. Masters thesis, Dublin, National College of Ireland.

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Abstract

The fast evolution of Android mobile devices has opened new possibilities for convenience and usefulness, but it has also given rise to Android malware, which poses a major danger to user security such as data theft. To discriminate between benign and malicious apps, effective detection systems are essential. Machine learning and Deep learning have emerged as a potent connect in this domain, capable of swiftly assessing Android application packages (APK files) and properly classifying applications. In this study, we examine the efficacy of various machine learning models and a deep learning model, with the Random Forest, Extra Tree, XGBoost, Stacking Classifier and Convolutional Neural Network (CNN), in detecting Android malware also Scikit Framework has been used for detecting malware in this research. Between these models, CNN stands out as the best performer, with respect to its precision, memory, and F1 scores to determine their correctness and dependability. This superior result demonstrates its capacity to reliably detect and categorise Android apps, emphasizing its importance in improving mobile security. This report offers critical insights into the state of Android malware detection using machine learning, offering a path forward to enhance Android security and shield users from potential security threats in the ever-evolving landscape of mobile technology.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Salahuddin, Jawad UNSPECIFIED
Uncontrolled Keywords:	Android malware; Machine learning; CNN; APK files; Scikit Framework
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Ciara O'Brien
Date Deposited:	03 Jun 2025 17:35
Last Modified:	03 Jun 2025 17:35
URI:	https://norma.ncirl.ie/id/eprint/7741

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