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Asia-Pacific Web Conference

APWeb 2013: Web Technologies and Applications pp 268–279Cite as

Identification of Sybil Communities Generating Context-Aware Spam on Online Social Networks

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7808))

Abstract

This paper presents a hybrid approach to identify coordinated spam or malware attacks carried out using sybil accounts on online social networks. It also presents an online social network data collection methodology, with a special focus on Facebook social network. The pages crawled from Facebook network are grouped according to users’ interests and analyzed to retrieve users’ profiles from each of them. As a result, based on the users’ page-likes behavior, a total number of six groups has been identified. Each group is treated separately and modeled using a graph structure, termed as profile graph, in which a node represents a profile and a weighted edge connecting a pair of profiles represents the degree of their behavior similarity. Behavior similarity is calculated as a function of common shared links, common page-likes, and cosine similarity of the posts, and used to determine weights of the edges of the profile graph. Louvain’s community detection algorithm is applied on the profile graphs to identify various communities. Finally, a set of statistical features identified in one of our previous works is used classify the obtained communities either as malicious or benign. The experimental results on a real dataset show that profiles belonging to a malicious community have high closeness-centrality representing high behavioral similarity, whereas those of a benign community have low closeness-centrality.

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Author information

Authors and Affiliations

  1. Center of Excellence in Information Assurance, King Saud University, Riyadh, Saudi Arabia

    Faraz Ahmed & Muhammad Abulaish

Authors
  1. Faraz Ahmed
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  2. Muhammad Abulaish
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Editor information

Editors and Affiliations

  1. Department of Information Engineering, Nagoya University, 464-8601, Nagoya, Japan

    Yoshiharu Ishikawa

  2. Department of Computer Science and Technology, Harbin Institute of Technology, 150006, Harbin, China

    Jianzhong Li

  3. School of Computer Science and Engineering, University of New South Wales, 2031, Sydney, NSW, Australia

    Wei Wang  & Wenjie Zhang  & 

  4. Department of Computing and Information Systems, University of Melbourne, 3052, Melbourne, VIC, Australia

    Rui Zhang

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Ahmed, F., Abulaish, M. (2013). Identification of Sybil Communities Generating Context-Aware Spam on Online Social Networks. In: Ishikawa, Y., Li, J., Wang, W., Zhang, R., Zhang, W. (eds) Web Technologies and Applications. APWeb 2013. Lecture Notes in Computer Science, vol 7808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37401-2_28

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  • DOI: https://doi.org/10.1007/978-3-642-37401-2_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37400-5

  • Online ISBN: 978-3-642-37401-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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