Android Mobile Malware Detection System Using Ensemble Learning

Ottakanchirathingal, Janish (2020) Android Mobile Malware Detection System Using Ensemble Learning. Masters thesis, Dublin, National College of Ireland.

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Abstract

Android is the most popular and dominant smartphone operating system in the market. The Google Play store in the Android platform contains more than one million android mobile applications that are downloaded and used by users for various purposes. Meanwhile, they have also become a prime target of unethical intrusions due to their open-source platform and popularity. In this research, we observed that traditional methods failed to detect sophisticated malware as they are incapable of detecting and predicting malware with variable attributes. In the last many years, Machine learning classifications strategies have been used to tackle these issues and this research observes that ensemble learning can produce the best results. Thus, in this research, we proposed an Ensemble learning classification model using Decision tree with Gradient Boosting algorithm that was trained on the Malgenome mobile malware dataset. The performance of the model is evaluated using metrics like accuracy and F1 and the model’s performance is compared with conventional models like Support Vector Machine (SVM) and Naive Bayes.
Keywords: Android, Malware, Ensemble Learning, Decision Tree with Gradient Boosting, SVM, Naive Bayes

Item Type:	Thesis (Masters)
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
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Dan English
Date Deposited:	27 Jan 2021 17:26
Last Modified:	27 Jan 2021 17:26
URI:	https://norma.ncirl.ie/id/eprint/4507

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