{"title":"Analysis and detection of fake profile over social network","authors":"V. Tiwari","doi":"10.1109/CCAA.2017.8229795","DOIUrl":null,"url":null,"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.","PeriodicalId":6627,"journal":{"name":"2017 International Conference on Computing, Communication and Automation (ICCCA)","volume":"41 1","pages":"175-179"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Conference on Computing, Communication and Automation (ICCCA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CCAA.2017.8229795","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
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.