Friend or Foe? Fake Profile Identification in Online Social Networks

Fire, Michael; Kagan, Dima; Elyashar, Aviad; Elovici, Yuval

doi:10.48550/arxiv.1303.3751

Cited by 3 publications

(7 citation statements)

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“…This layer consists of different security and privacy settings offered by various OSN operators. However, in many cases the average OSN user does not know or is unaware of the best way to "lock" his or her profile, instead leaving the privacy settings on default, which often provides insufficient protection [49,76]. To assist such users, security companies and academic researchers have developed solutions, such as Privacy Scanner for Facebook [99], ZoneAlarm Privacy Scan [23], and The Social Privacy Protector [48], all of which can assist OSN users in improving their privacy settings.…”

Section: Discussionmentioning

confidence: 99%

“…Some OSNs also support extra security configurations which enable the user to activate secure browsing, receive login notifications, and establish other safety features [118]. However, many OSN users still simply maintain the default privacy settings, letting their data be exposed to strangers [49,76].…”

Section: Social Network Operator Solutionsmentioning

confidence: 99%

“…Fire et al [48] also presented The Social Privacy Protector application which assists Facebook users in identifying fake profiles among their friends. They used the dataset created by The Social Privacy Protector application and developed machine learning classifiers which can identify fake profiles on Facebook [49]. Recently, Wang et al [147] presented a system which can detect fake profiles based on analyzing clickstream models.…”

Section: Academic Solutionsmentioning

confidence: 99%

“…In many social networks, like Facebook, the default privacy settings are insufficient. Yet a re-cent study has showed that many Facebook users tend to stay with their default privacy settings [49]. In order for users to better protect themselves on Facebook and in other OSNs, we recommend modifying the privacy settings so that users' personal data will be exposed only to themselves, or at most to their friends only (for example, see Fig.…”

Section: Recommendationsmentioning

confidence: 99%

“…Nevertheless, both regulating authorities and public groups try to address privacy concerns and make them a part of public discourse and consideration [8]. Today there are additional protection mechanisms which include defenses against spammers [4,13,14,31,77,130,146], fake profiles [20,29,48,49,50,136,147,148,159,160], and other threats. For example, security companies like Check Point [22], Websense [149], and Infoglide [70] offer social tools to protect users in the OSN world.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Online Social Networks: Threats and Solutions

Fire¹,

Goldschmidt

Elovici³

2014

IEEE Commun. Surv. Tutorials

Self Cite

269

126

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Many online social network (OSN) users are unaware of the numerous security risks that exist in these networks, including privacy violations, identity theft, and sexual harassment, just to name a few. According to recent studies, OSN users readily expose personal and private details about themselves, such as relationship status, date of birth, school name, email address, phone number, and even home address. This information, if put into the wrong hands, can be used to harm users both in the virtual world and in the real world. These risks become even more severe when the users are children. In this paper we present a thorough review of the different security and privacy risks which threaten the well-being of OSN users in general, and children in particular. In addition, we present an overview of existing solutions that can provide better protection, security, and privacy for OSN users. We also offer simple-to-implement recommendations for OSN users which can improve their security and privacy when using these platforms. Furthermore, we suggest future research directions.Comment: Draft Versio

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Section: Discussionmentioning

confidence: 99%

Section: Social Network Operator Solutionsmentioning

confidence: 99%

Section: Academic Solutionsmentioning

confidence: 99%

Section: Recommendationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Online Social Networks: Threats and Solutions

Fire¹,

Goldschmidt

Elovici³

2014

IEEE Commun. Surv. Tutorials

Self Cite

269

126

View full text Add to dashboard Cite

show abstract

How Safe You Are on Social Networks?

Singh

2022

Cybernetics and Systems

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Large-Scale Shill Bidder Detection in E-commerce

Fire¹,

Puzis²,

Kagan³

et al. 2022

Preprint

Self Cite

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User feedback is one of the most effective methods to build and maintain trust in electronic commerce platforms. Unfortunately, dishonest sellers often bend over backwards to manipulate users' feedback or place phony bids in order to increase their own sales and harm competitors. The black market of user feedback, supported by a plethora of shill bidders, prospers on top of legitimate electronic commerce. In this paper we investigate the ecosystem of shill bidders based on large-scale data by analyzing hundreds of millions of users who performed billions of transactions, and we propose a machine-learning-based method for identifying communities of users that methodically provide dishonest feedback. Our results show that (1) shill bidders can be identified with high precision based on their transaction and feedback statistics; and (2) in contrast to legitimate buyers and sellers, shill bidders form cliques to support each other.

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Friend or Foe? Fake Profile Identification in Online Social Networks

Cited by 3 publications

References 0 publications

Online Social Networks: Threats and Solutions

Online Social Networks: Threats and Solutions

How Safe You Are on Social Networks?

Large-Scale Shill Bidder Detection in E-commerce

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