Propagation model of smartphone worms based on semi-Markov process and social relationship graph

Peng,S, Wu,M, Wang,G and Yu,S 2014, Propagation model of smartphone worms based on semi-Markov process and social relationship graph, Computers and Security, vol. 44, pp. 92-103, doi: 10.1016/j.cose.2014.04.006.

Attached Files
Name Description MIMEType Size Downloads

Title Propagation model of smartphone worms based on semi-Markov process and social relationship graph
Author(s) Peng,S
Yu,SORCID iD for Yu,S
Journal name Computers and Security
Volume number 44
Start page 92
End page 103
Total pages 12
Publisher Elsevier
Place of publication Oxon, United Kingdom
Publication date 2014-07
ISSN 0167-4048
Keyword(s) Propagation model
Semi-Markov process
Social relationship graph
Science & Technology
Computer Science, Information Systems
Computer Science
Summary Smartphone applications are getting more and more popular and pervasive in our daily life, and are also attractive to malware writers due to their limited computing source and vulnerabilities. At the same time, we possess limited understanding of our opponents in cyberspace. In this paper, we investigate the propagation model of SMS/MMS-based worms through integrating semi-Markov process and social relationship graph. In our modeling, we use semi-Markov process to characterize state transition among mobile nodes, and hire social network theory, a missing element in many previous works, to enhance the proposed mobile malware propagation model. In order to evaluate the proposed models, we have developed a specific software, and collected a large scale real-world data for this purpose. The extensive experiments indicate that the proposed models and algorithms are effective and practical. © 2014 Elsevier Ltd. All rights reserved.
Language eng
DOI 10.1016/j.cose.2014.04.006
Field of Research 080502 Mobile Technologies
Socio Economic Objective 890103 Mobile Data Networks and Services
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2014, Elsevier
Persistent URL

Document type: Journal Article
Collection: School of Information Technology
Connect to link resolver
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 18 times in TR Web of Science
Scopus Citation Count Cited 20 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 267 Abstract Views, 2 File Downloads  -  Detailed Statistics
Created: Wed, 22 Apr 2015, 15:43:48 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact