2015
DOI: 10.1007/978-3-319-21009-4_45
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Intelligent Sybil Attack Detection on Abnormal Connectivity Behavior in Mobile Social Networks

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Cited by 6 publications
(6 citation statements)
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“…Social networks enable users to share photos, videos, music, and other information with selected friends or with the public at large [14] [15]. Some of the well-known social networks such as Facebook, Twitter, LinkedIn, Instagram, YouTube, and Myspace provide convenient options to maintain connections with other users and friends that have shared interests [16] [17] [18].…”
Section: A Overview Of Osnsmentioning
confidence: 99%
“…Social networks enable users to share photos, videos, music, and other information with selected friends or with the public at large [14] [15]. Some of the well-known social networks such as Facebook, Twitter, LinkedIn, Instagram, YouTube, and Myspace provide convenient options to maintain connections with other users and friends that have shared interests [16] [17] [18].…”
Section: A Overview Of Osnsmentioning
confidence: 99%
“…However, we argue that traffic jams may produce continuous gathering in urban zones. Other graph-based solution has been proposed for mobile online social networks in [3], where the authors use a connection analysis to differentiate honest versus fake nodes. Finally, in the context of mobile crowdsourcing, recent work proposes a passive and active checking scheme that verify traffic volume, signal strength and network topology [2], differentiating nodes using an adaptive threshold.…”
Section: Managing Sybil Attacks and Collusionsmentioning
confidence: 99%
“…However, malicious users can easily overcome such challenges by sending several friend requests to legitimate users. 2,9,10,12,18,23,30,31 The real difficulty experienced by malicious users getting legitimate users to befriend them first or to accept requests with a high probability. Another graph-based Sybil detection algorithm is SybilRank, 32 which ranks nodes on the basis of the degree-normalized probability of a short random walk that begins from a legitimate node.…”
Section: Related Studiesmentioning
confidence: 99%
“…These techniques depend on an assumption of limited attack edges, because Sybil nodes are assumed to find it difficult to establish relationships with legitimate user nodes. However, malicious users can easily overcome such challenges by sending several friend requests to legitimate users . The real difficulty experienced by malicious users getting legitimate users to befriend them first or to accept requests with a high probability.…”
Section: Related Studiesmentioning
confidence: 99%
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