FACT - Towards Automatic Real Time Identification of Malicious Posts on Facebook

Authors(2) :-Kanagalakshmi V, Rubygnanaselvam

Online Social Networks (OSNs) witness a rise in user activity whenever a news-making event takes place. Cyber criminals exploit this spur in user-engagement levels to spread malicious content that compromises system reputation, causes financial losses and degrades user experience. In this paper to detect the malicious contents in the Facebook post using the annotation based approach and compared with the existing WebOfTrust (WOT) method. Social malware posts typically include at least one embedded URL link, since without such a link. The posts cannot lure and hurt users or propagate virally. This approach is trying to detect such posts on the walls and news feeds of Facebook users, and alert users exposed to social malware so that they do not click through on the URLs included in the posts. Final part contains the comparison between the WOT and Fact model on detecting malicious Facebook posts.This model can be used in FACT model to identify the malicious post on eltime,

Authors and Affiliations

Kanagalakshmi V
Bishop Appasamy College of Arts and Science, Race course Road, Coimbatore, Tamilnadu, India
Rubygnanaselvam
Bishop Appasamy College of Arts and Science, Race course Road, Coimbatore, Tamilnadu, India

Online Social Networks, WebOfTrus, FACT Model, TDL, WOT

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Publication Details

Published in : Volume 2 | Issue 6 | November-December 2017
Date of Publication : 2017-12-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 305-309
Manuscript Number : CSEIT1726109
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Kanagalakshmi V, Rubygnanaselvam, "FACT - Towards Automatic Real Time Identification of Malicious Posts on Facebook", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 6, pp.305-309, November-December-2017.
Journal URL : http://ijsrcseit.com/CSEIT1726109

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