A survey of data mining and social network analysis based anomaly detection techniques

Abstract With the increasing trend of online social networks in different domains, social network analysis has recently become the center of research. Online Social Networks (OSNs) have fetched the interest of researchers for their analysis of usage as well as detection of abnormal activities. Anomalous activities in social networks represent unusual and illegal activities exhibiting different behaviors than others present in the same structure. This paper discusses different types of anomalies and their novel categorization based on various characteristics. A review of number of techniques for preventing and detecting anomalies along with underlying assumptions and reasons for the presence of such anomalies is covered in this paper. The paper presents a review of number of data mining approaches used to detect anomalies. A special reference is made to the analysis of social network centric anomaly detection techniques which are broadly classified as behavior based, structure based and spectral based. Each one of this classification further incorporates number of techniques which are discussed in the paper. The paper has been concluded with different future directions and areas of research that could be addressed and worked upon.

• Publication year

  2016

• Venue

  Egyptian Informatics Journal

• Publication date

  2016-07-01

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1016/J.EIJ.2015.11.004
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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