Analysis and detection of fake profile over social network

V. Tiwari
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引用次数: 11

Abstract

Latest developments have seen exponential increase in clientele of social networks. Facebook has 1.5 billion users. More than 10 million likes and shares are executed daily. Many other networks like ‘linkedin’, ‘Instagram’, ‘Pinterest’, ‘Twitter’ etc are fast growing. Growth of social networks has given rise to a very high number of fake user profiles created out of ulterior motives. Fake profiles are also known as Sybils or social Bots. Many such profiles try and befriend the benign users with an ultimate aim of gaining access to privileged information. Social engineering is the primary cause of threats in any Online Social Network (OSN). This paper reviews many methods to detect the fake profiles and their online social bot. Multi agent perspective of online social networks has also been analysed. It also discusses the Machine learning methods useful in profile creation and analysis.
社交网络虚假个人资料的分析与检测
最新的发展趋势是社交网络用户呈指数级增长。Facebook拥有15亿用户。每天有超过1000万的点赞和分享。许多其他网络,如linkedin、Instagram、Pinterest、Twitter等都在快速增长。社交网络的发展导致了大量别有用心的虚假用户资料的出现。虚假的个人资料也被称为Sybils或社交机器人。许多这样的配置文件试图与善意的用户交朋友,最终目的是获得特权信息的访问权。社交工程是任何在线社交网络(OSN)威胁的主要原因。本文综述了许多检测虚假个人资料及其在线社交机器人的方法。本文还分析了在线社交网络的多智能体视角。它还讨论了在概要文件创建和分析中有用的机器学习方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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