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Rebin A. Saeed Shareef M. Shareef

Abstract

The online social network clients are exposed to various weaknesses dangers deliberately by actualizing social designing systems. Cyber-Criminals are focusing on the social designing method regularly explore the climate of a client. Nonetheless, ebb and flow research centers around the specialized estimation of how to kill or forestall dangers totally in the online informal community climate. Along these lines, the online informal organization frameworks utilize pertinent models to make highlights for additional examination. Facebook, in the ongoing past, has buckled down and put intensely in creating calculations that can decide an up-and-coming digital assault dependent on client's conduct and qualities on the stage. Despite the fact that Facebook has done important specialized measures to limit dangers however much as could be expected, there is as yet a hole for additional examinations, which try to outfit the mechanized algorithmic forecast utilizing man-made consciousness to decide the chance of an assault or a danger. This exploration utilizes AI strategies to show how an AI-based calculation of the client's conduct attributes, insights, and human science feelings would help recognize highlights that further gotten extremely significant in deciding a person's weakness to social designing dangers and assaults. The point is to; fundamentally study the conventional viewpoint and never-ending viewpoint responses towards socially designed dangers day by day. This exploration utilizes a near examination of the speculations and the essential information discoveries to show that specific practices of Facebook clients are a danger to different clients. The outcomes have demonstrated how AI calculations work in distinguishing misleading messages and con artists through AI methods.

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How to Cite

Rebin A. Saeed, & Shareef M. Shareef. (2021). Prediction of The Security Threats in Social Media Using Artificial Intelligence . QALAAI ZANIST JOURNAL, 6(1), 1013–1034. https://doi.org/10.25212/lfu.qzj.6.1.38

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