Detection of Account Cloning in Online Social Networks

Punkamol, Dechana; Marukatat, Rangsipan

doi:10.1109/ieecon48109.2020.229558

Cited by 6 publications

(2 citation statements)

References 2 publications

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“…The precision of fake accounts was 0.8, and real accounts was 0.92, so the approach was proved good after about 50 iterations. Punkamol and Marukatat [24] proposed a method that identified account cloning in Twitter based on user profiles, friends, "follower networks," and posting behaviors. "Twitter Crawler, Attribute Extractor, and Cloning Detector" were parts of the framework.…”

Section: Role Of Machine Learning In Fake Profile Detectionmentioning

confidence: 99%

Exploring machine learning techniques for fake profile detection in online social networks

Bharti

Gill

Gulia

2023

IJECE

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<span lang="EN-US">The online social network is the largest network, more than 4 billion users use social media and with its rapid growth, the risk of maintaining the integrity of data has tremendously increased. There are several kinds of security challenges in online social networks (OSNs). Many abominable behaviors try to hack social sites and misuse the data available on these sites. Therefore, protection against such behaviors has become an essential requirement. Though there are many types of security threats in online social networks but, one of the significant threats is the fake profile. Fake profiles are created intentionally with certain motives, and such profiles may be targeted to steal or acquire sensitive information and/or spread rumors on online social networks with specific motives. Fake profiles are primarily used to steal or extract information by means of friendly interaction online and/or misusing online data available on social sites. Thus, fake profile detection in social media networks is attracting the attention of researchers. This paper aims to discuss various machine learning (ML) methods used by researchers for fake profile detection to explore the further possibility of improvising the machine learning models for speedy results.</span>

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Section: Role Of Machine Learning In Fake Profile Detectionmentioning

confidence: 99%

Exploring machine learning techniques for fake profile detection in online social networks

Bharti

Gill

Gulia

2023

IJECE

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show abstract

“…However, in case the extracted profile was fake, the photo of this profile has already satisfied the similarity threshold of the face recognition module. Thus, this means that this kind of profile is a clone of a real user profile since it would have both the same data and a photo of the real person that SODA was looking for [34,35].…”

Section: A Tool For Analyzing User Datamentioning

confidence: 99%

Social network data analysis to highlight privacy threats in sharing data

et al. 2022

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Social networks are a vast source of information, and they have been increasing impact on people’s daily lives. They permit us to share emotions, passions, and interactions with other people around the world. While enabling people to exhibit their lives, social networks guarantee their privacy. The definitions of privacy requirements and default policies for safeguarding people’s data are the most difficult challenges that social networks have to deal with. In this work, we have collected data concerning people who have different social network profiles, aiming to analyse privacy requirements offered by social networks. In particular, we have built a tool exploiting image-recognition techniques to recognise a user from his/her picture, aiming to collect his/her personal data accessible through social networks where s/he has a profile. We have composed a dataset of 5000 users by combining data available from several social networks; we compared social network data mandatory in the registration phases, publicly accessible and those retrieved by our analysis. We aim to analyse the amount of extrapolated data for evaluating privacy threats when users share information on different social networks to help them be aware of these aspects. This work shows how users data on social networks can be retrieved easily by representing a clear privacy violation. Our research aims to improve the user’s awareness concerning the spreading and managing of social networks data. To this end, we highlighted all the statistical evaluations made over the gathered data for putting in evidence the privacy issues.

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A novel defense mechanism to protect users from profile cloning attack on Online Social Networks (OSNs)

Jethava

Rao

2022

Peer-to-Peer Netw. Appl.

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Detection of Account Cloning in Online Social Networks

Cited by 6 publications

References 2 publications

Exploring machine learning techniques for fake profile detection in online social networks

Exploring machine learning techniques for fake profile detection in online social networks

Social network data analysis to highlight privacy threats in sharing data

A novel defense mechanism to protect users from profile cloning attack on Online Social Networks (OSNs)

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