NORMA eResearch @NCI Library

Malware Detection on Android using Adaboost Algorithm

Pallippattu Mathai, Liston (2021) Malware Detection on Android using Adaboost Algorithm. Masters thesis, Dublin, National College of Ireland.

[img]
Preview
PDF (Master of Science)
Download (686kB) | Preview
[img]
Preview
PDF (Configuration manual)
Download (2MB) | Preview

Abstract

Advanced expansion of technologies and mobile devices has directed in to key cyber-attacks in the contemporary ages. Android is a famous operating platform, which made use in tablets and smartphones and also befit a fundamental target of untrustworthy obligations performed by various malwares. It is reported there is over 50 billion android OS download and more than 1.3 millions of android applications accessible in the official market of google and the popularity is still increasing. Broadening of the consumers in this particular operating system welfares the enemies to produce immense malwares which distress defectively with time. This study observes that, even though there are methods to detect malware in android system using machine learning techniques where the accuracy seems to be low. Machine learning Is an approach used in the past years to detect malwares which not produce a better result for the recent malwares developed (Yerima et al., 2015). Ensemble learning is an approach similar to machine learning which can give a healthier outcome, this encourage me to create a system to detect the malware in android using one of the ensemble learning alogorithm named “Adaboost” which is a boosting technique. Boosting is a method of merging distinctive low accuracy model which create high accuracy model. Mentioned Adaboost algorithm will be trained on Android data to understand how efficient is the algorithm to detect malware on android.

Item Type: Thesis (Masters)
Uncontrolled Keywords: Malware; Android; Machine Learning; Ensemble Learning; Boosting; Adaboost
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: Clara Chan
Date Deposited: 01 Nov 2021 13:08
Last Modified: 01 Nov 2021 13:08
URI: http://norma.ncirl.ie/id/eprint/5123

Actions (login required)

View Item View Item