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Adaptive Graph-based algorithms for Spam Detection in Social Networks
KTH, School of Electrical Engineering (EES), Communication Networks. (SCS/ICT)
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS. (SCS/ICT)
2016 (English)Report (Other academic)
Abstract [en]

As Online Social Networks (OSNs) continue to grow in popularity, a spam marketplace has emerged that includes services selling fraudulent accounts, as well as acts as nucleus of spammers who propagate large-scale spam campaigns. In the past years, researchers developed approaches to detect spam such as URL blacklisting, spam traps and even crowdsourcing for manual classification. Although previous work has shown the effectiveness of using statistical learning to detect spam, existing work employs supervised schemes that require labeled training data. In addition to the heavy training cost, it is difficult to obtain a comprehensive source of ground truth for measurement. In contrast to existing work, in this paper we present a novel graph-based approach for spam detection. Our approach is unsupervised, hence it diminishes the need of labeled training data and training cost. Particularly, our approach can effectively detect the spam in large-scale OSNs by analyzing user behaviors using graph clustering technique. Moreover, our approach continuously updates detected communities to comply with dynamic OSNs where interactions and activities are evolving rapidly. Extensive experiments using Twitter datasets show that our approach is able to detect spam with accuracy 92.3\%. Furthermore, our approach has false positive rate that is less than 0.3\% that is less than half of the rate achieved by the state-of-the-art approaches.

Place, publisher, year, edition, pages
KTH Royal Institute of Technology, 2016.
Keyword [en]
Distributed Systems, Graphs and networks, Graph algorithms, Spam Detection, Social Networks
National Category
Computer Systems
URN: urn:nbn:se:kth:diva-193135OAI: diva2:998690

QC 20161005

Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2016-10-05Bibliographically approved

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