Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/77225
Full metadata record
DC FieldValueLanguage
dc.contributor.authorLoh, Jing Kai
dc.date.accessioned2019-05-17T08:38:50Z
dc.date.available2019-05-17T08:38:50Z
dc.date.issued2019
dc.identifier.urihttp://hdl.handle.net/10356/77225
dc.description.abstractMobile phones especially smartphone is now an essential item in today’s society, granting user’s ability to perform tasks and brining convenience to its user. While IOS popularity is constantly growing, android still commands much of the market share and thus has been seen as a lucrative market for malicious actors to benefit off this huge market. Although a variety of methods exist to protect users from these malicious actors, those solutions tend to have negative drawbacks to them. Thus, new way to be able to detect these malwares is needed. In this project, the focus will be on the development of malware detection tool that will be located on the android platform. It will use the features located in the APK alongside machine learning to predict if an APK is malicious or benign. While certain aspect of the machine learning and deployment to android generated good outcome, several issues were identified during the development and testing.en_US
dc.format.extent40 p.en_US
dc.language.isoenen_US
dc.rightsNanyang Technological University
dc.subjectDRNTU::Engineering::Computer science and engineering::Computing methodologies::Artificial intelligenceen_US
dc.titleMalware detection application for android using machine learningen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorLiu Yangen_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Science)en_US
item.fulltextWith Fulltext-
item.grantfulltextrestricted-
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
Files in This Item:
File Description SizeFormat 
1622074H FYP Final Report.pdf
  Restricted Access
1.82 MBAdobe PDFView/Open

Page view(s)

107
Updated on Jun 13, 2021

Download(s) 50

27
Updated on Jun 13, 2021

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.