2018 IEEE 11th International Conference on Cloud Computing (CLOUD) 2018
DOI: 10.1109/cloud.2018.00020
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Oases: An Online Scalable Spam Detection System for Social Networks

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Cited by 6 publications
(4 citation statements)
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“…Therefore, the prime challenge to defeat spammers and spam content is to be able to distinguish spam data from genuine data in a real-time fashion. The conventional spam classifiers are centralized and examine an offline log of message histories [51]. However, such designs are not adaptable to P2P architecture where data gets generated in a distributed manner.…”
Section: Spam Protectionmentioning
confidence: 99%
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“…Therefore, the prime challenge to defeat spammers and spam content is to be able to distinguish spam data from genuine data in a real-time fashion. The conventional spam classifiers are centralized and examine an offline log of message histories [51]. However, such designs are not adaptable to P2P architecture where data gets generated in a distributed manner.…”
Section: Spam Protectionmentioning
confidence: 99%
“…DHT-based group management techniques provide a key component toward timely data aggregation and classification. To address spam protection, Oases [51] and Sifter [52] utilize DHT-based group management. Groups are formed around OSN users who are willing to participate in spam protection.…”
Section: Spam Protectionmentioning
confidence: 99%
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