dc.contributor.author | Koggalahewa, D | |
dc.contributor.author | Xu, Y | |
dc.contributor.author | Foo, E | |
dc.date.accessioned | 2021-02-04T06:28:09Z | |
dc.date.available | 2021-02-04T06:28:09Z | |
dc.date.issued | 2020 | |
dc.identifier.isbn | 9781728125473 | |
dc.identifier.doi | 10.1109/SSCI47803.2020.9308315 | |
dc.identifier.uri | http://hdl.handle.net/10072/401683 | |
dc.description.abstract | Spammers use Online Social Networks (OSNs) as a popular platform for spreading malicious content and links. The nature of OSNs allows the spammers to bypass the combating techniques by changing their behaviours. Classification based approaches are the most common technique for spam detection. 'Data labelling' 'spam drift' 'imbalanced datasets' and 'data fabrication' are the most common limitations of classification techniques that hinder the accuracy of spam detection. The paper presents a two-stage fully unsupervised approach using a user's peer acceptance within OSN to distinguish spammers from genuine users. User's common shared interest over multiple topics and the mentioning behaviour are used to derive the peer acceptance. The contribution of the paper is a pure unsupervised method to detect spammers based on users' peer acceptance without labelled datasets. Our unsupervised approach is able to achieve 95.9% accuracy without the need for labelling. | |
dc.description.peerreviewed | Yes | |
dc.publisher | IEEE | |
dc.relation.ispartofconferencename | 2020 IEEE Symposium Series on Computational Intelligence (SSCI) | |
dc.relation.ispartofconferencetitle | 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020 | |
dc.relation.ispartofdatefrom | 2020-12-01 | |
dc.relation.ispartofdateto | 2020-12-04 | |
dc.relation.ispartofpagefrom | 71 | |
dc.relation.ispartofpageto | 78 | |
dc.subject.fieldofresearch | Information and computing sciences | |
dc.subject.fieldofresearchcode | 46 | |
dc.title | Two-stage Unsupervised Approach for Combating Social Spammers | |
dc.type | Conference output | |
dc.type.description | E1 - Conferences | |
dcterms.bibliographicCitation | Koggalahewa, D; Xu, Y; Foo, E, Two-stage Unsupervised Approach for Combating Social Spammers, 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, 2020, pp. 71-78 | |
dc.date.updated | 2021-02-04T05:29:08Z | |
dc.description.version | Accepted Manuscript (AM) | |
gro.rights.copyright | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |
gro.hasfulltext | Full Text | |
gro.griffith.author | Foo, Ernest | |