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Clustering-based anomaly detection for smartphone applications

  • GSM-LASMIS

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Nowadays, Smartphones have been widely used due to their capabilities in communication and multimedia processing. Smartphones provide access to a tremendous amount of sensitive information related to business, such as customer contacts, financial data, and Intranet networks. Hence, the Internet of the future will be mobile Internet. However, threat of malicious software has become an important factor in the smartphones security. In this paper, a new behavior-based malware detection framework using three clustering methods (PAM, DBSCAN and t-distribution) is proposed. Experimental results show that the approach has high detection rate and low rate of false positive and false negative.

Original languageEnglish
Title of host publicationIEEE/IFIP NOMS 2014 - IEEE/IFIP Network Operations and Management Symposium
Subtitle of host publicationManagement in a Software Defined World
PublisherIEEE Computer Society
ISBN (Print)9781479909131
DOIs
Publication statusPublished - 1 Jan 2014
Externally publishedYes
EventIEEE/IFIP Network Operations and Management Symposium: Management in a Software Defined World, NOMS 2014 - Krakow, Poland
Duration: 5 May 20149 May 2014

Publication series

NameIEEE/IFIP NOMS 2014 - IEEE/IFIP Network Operations and Management Symposium: Management in a Software Defined World

Conference

ConferenceIEEE/IFIP Network Operations and Management Symposium: Management in a Software Defined World, NOMS 2014
Country/TerritoryPoland
CityKrakow
Period5/05/149/05/14

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