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Group Anomaly Detection: Past Notions, Present Insights, and Future Prospects

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Abstract

Anomaly detection has evolved as a successful research subject in the areas such as bibliometrics, informatics and computer networks including security-based and social networks. Almost all existing anomaly detection techniques have some limitations and do not focus specifically on detecting anomalous groups. Anomaly detection is also a crucial problem in processing large-scale datasets when our goal is to find abnormal values or unusual events. The authors decided to survey existing group anomaly detection techniques because there is a need to consider group anomalies for mitigation of risks, prevention of malicious collaborative activities, and other interesting explanatory insights by identifying groups that are not consistent with regular group patterns. In this research, we bifurcated group anomaly detection techniques into activity-based and graph-based methods. The graphical methodologies are then further classified under static versus dynamic and attributed versus plain graph methods. We have also listed the datasets used in various studies to detect group anomalies along with detected anomalies and the various performance measures used to validate the results. Towards the end, we have provided various applications of group anomaly detection and the research challenges that group anomaly detection presents to the scientific community and enlisted some of the future trends for this particular research area.

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Notes

  1. One Class-Support vector machine detect anomalous behavior [103].

  2. Density-based local outliers [104].

  3. Statistics of extremes: theory and applications [105].

  4. Structural comparison measure of objects by Jeh and Widom [106].

  5. Ranking algorithm for web search engine proposed by Brin and Page [107].

  6. Model measures the compression of data proposed by Rissanen [108].

  7. “The law of anomalous numbers” by Frank Benford [109].

  8. Singular value decomposition matrix for determining rank and range of data proposed by Golub and Reinsch [110].

  9. en.wikipedia.org/wiki/Kolmogorov–Smirnov_test.

  10. metacademy.org/graphs/concepts/f_measure.

  11. Modularity by Newman and Girvan [111].

  12. en.wikipedia.org/wiki/Perplexity.

  13. H-index measures citation impact of the author's publications by Hirsch [112].

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Authors and Affiliations

  1. Department of Computer Science and Software Engineering, International Islamic University, Islamabad, Pakistan

    Aqeel Feroze, Ali Daud & Tehmina Amjad

  2. Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, Jeddah University, Jeddah, Saudi Arabia

    Ali Daud

  3. Department of Information Technology, Faculty of Information Technology and Engineering, The University of Haripur, Haripur, Pakistan

    Malik Khizar Hayat

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Feroze, A., Daud, A., Amjad, T. et al. Group Anomaly Detection: Past Notions, Present Insights, and Future Prospects. SN COMPUT. SCI. 2, 219 (2021). https://doi.org/10.1007/s42979-021-00603-x

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