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003			MY-KuUP
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008			230404t20222022my a|||fr|||| 000 0 eng d
020			_aTHE0009556 (Local) _qHardback
040			_aUMP _beng _cUMP _erda
090			_aFKOM .S23 2022 r Thesis
100	1		_aSadat, S M Nazmus, _eauthor.
245	1	2	_aA reliable friendship mechanism for online social network exploiting pre and post-filtering approach / _cS M Nazmus Sadat
264		1	_aKuantan, Pahang : _bUMP, _c2022
264		1	_c© 2022
300			_axiii, 156 pages : _billustrations (some color) ; _c30 cm. + _e1 CD-ROM
336			_2rdacontent _atext
336			_2rdacontent _atext
337			_aunmediated _2rdamedia
337			_acomputer _2rdamedia
338			_2rdacarrier _avolume
338			_acomputer disc _2rdacarrier
347			_2rda _atext file _bPDF
500			_aFaculty of Computing
502			_aThesis (Doctor of Philosophy) -- Universiti Malaysia Pahang – 2022
504			_aIncludes bibliographical references
520	3		_aOnline social networks are becoming increasingly popular, and their uses are growing day by day. It is an integral part of our daily lives and an incomparable medium to communicate with family, friends, and professionals in the interest of personal and professional purposes. Because of these features, the online social network contains a great deal of information that individuals can share with one another. Therefore, personal information is easily disclosed online and is misused by unreliable friends or associates without the users’ awareness. Unfortunately, certain functional issues have not been addressed regarding the automatic filtering approach in initiating friendships and users’ interactions with their online associates. Furthermore, a user cannot properly scrutinize the anomalous behaviour of other users over the time variant, which can clearly engage them in malpractices. In order to address these issues, the proposed study develops a reliable friendship mechanism for online social networks by utilizing automated two-phased (pre and post) filtering approaches to determine reliable friends and monitor their behavioral activities. In the prefiltering approach, a user can select a friend using a reliable mechanism, which consists of two choices: attribute-based and model-based. A machine learning (ML) enabled postfiltering approach is designed to determine suspicious and unwanted behavioral activities. Finally, the proposed mechanism incorporates pre and post-filtering approaches, resulting in a novel hybrid approach that can accomplish the purpose of the study. The empirical analysis shows some significant comparison data towards the hybrid approach (after incorporating pre and post-filtering approaches), where the users’ perceptions of the proposed approach exceed the other competing approaches significantly. As shown in the proportionate mean values, the proposed hybrid approach achieved the highest ratio of 90.64%, with pre-filtering accounting for 69.76% and post-filtering standing for 70.56%, while the existing approach had the lowest proportionate mean value at 47.60%. The outcomes of this study are expected to assist OSN providers, research communities, and ICT authorities in providing a standard solution for selecting reliable friends and avoiding their malpractices in OSN.
610	2	0	_aFaculty of Computing _xDissertations
650		0	_aUniversities and colleges _xDissertations
650		0	_aTheses
942			_2lcc _cTHESIS