International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Science , Computer Science and Engineering

Volume5,February 2018,

Topic : Detection of Compromised Accounts in Online Social Network

Authors:Sneha Rane || Megha Ainapurkar, Ameya Wadekar

Abstract:Compromised accounts are of a severe risk to the social network users. People nowadays are mostly dependent on Online Social Networks. While some persistent spams feat the relationship between the users by spreading spams. Therefore time to time detection of the compromised accounts is a necessity. In this paper, we will study different social user behaviour and detect the compromised accounts and spam users. Spam behaviour in social networks has a wide range of illegal activities. Such activities need to be evaluated and effect of spam users needs to be reduced. To reduce such effects, we require proper detection strategy. We validate the effectiveness of this behaviour by collecting the clickstream data on a social network website. Social behaviour reflects the user's behaviour online. While a legitimate user coordinates its social behaviour carefully, it is hard for the fake users to pretend to be affected. Different studies are performed in spam behaviour analysis and define a structure for spam account detection..

Keywords: Clickstream, compromised accounts, social networks

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DOI: 01.1617/vol5/iss2/pid96072


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Editor-in-Chief

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Dr. Allon Guez
Professor, Drexel University,
USA


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IMPACT FACTOR: 4.890

ISSN(Online):2394-2320

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Thomson Reuters ID : q-6288-2016.
ORCiD Research ID : 0000-0001-9540-6799

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ACCEPTANCE RATIO

ACCEPTANCE RATIO: 28.69%
ARTICLES PUBLISHED:0521
PAPER RECEIVED:01730
Journal Code : IJERCSE
Electronic ISSN : 2394-2320
Impact Factor : 4.890
Frequency : monthly
Contact : info@ijercse.com


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