Please submit manuscripts in either of the following two submission systems

    ScholarOne Manuscripts

  • ScholarOne
  • 勤云稿件系统

  • 登录

Search by Issue

  • 2024 Vol.31
  • 2023 Vol.30
  • 2022 Vol.29
  • 2021 Vol.28
  • 2020 Vol.27
  • 2019 Vol.26
  • 2018 Vol.25
  • 2017 Vol.24
  • 2016 vol.23
  • 2015 vol.22
  • 2014 vol.21
  • 2013 vol.20
  • 2012 vol.19
  • 2011 vol.18
  • 2010 vol.17
  • 2009 vol.16
  • No.1
  • No.2

Supervised by Ministry of Industry and Information Technology of The People's Republic of China Sponsored by Harbin Institute of Technology Editor-in-chief Yu Zhou ISSNISSN 1005-9113 CNCN 23-1378/T

期刊网站二维码
微信公众号二维码
Related citation:Le Ha,Guo-Zi Sun.An Automatically Filtering Blacklist Model of Social Network Based on Semantic Web[J].Journal of Harbin Institute Of Technology(New Series),2014,21(6):67-73.DOI:10.11916/j.issn.1005-9113.2014.06.013.
【Print】   【HTML】   【PDF download】   View/Add Comment  Download reader   Close
←Previous|Next→ Back Issue    Advanced Search
This paper has been: browsed 1888times   downloaded 935times 本文二维码信息
码上扫一扫!
Shared by: Wechat More
An Automatically Filtering Blacklist Model of Social Network Based on Semantic Web
Author NameAffiliation
Le Ha College of Computer, Nanjing University of Posts & Telecommunications, Nanjing 210003, China 
Guo-Zi Sun College of Computer, Nanjing University of Posts & Telecommunications, Nanjing 210003, China
Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing 210003, China 
Abstract:
According to the features of the semantic web technology, it is very suitable to solve the security issue of the current social network environment. Firstly, in this paper, it extends the existing ontology model of the social network with some relevant classes, and introduces a brand new ontology which is used to represent the malicious information. After introducing these models, a method of identifying the malicious message is raised. Finally, the experiments and simulations analyze the feasibility of the whole system. The results validate that the malicious users can be automatically filtered, and some worthy digital evidence can be effectively provided to forensic investigators.
Key words:  social network  semantic web  ontology  OWL  digital evidence
DOI:10.11916/j.issn.1005-9113.2014.06.013
Clc Number:TP391.7
Fund:

LINKS