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Applying Data Mining in Surveillance: Detecting Suspicious Activity on Social Networks

Author

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  • Fouzi Harrag

    (Ferhat Abbas University, Algeria)

  • Ali Alshehri

    (Royal Comission for Riyadh City, Saudi Arabia)

Abstract

In the current times where human safety is threatened by man-made and natural calamities, surveillance systems have gained immense importance. But, even in presence of high definition (HD) security cameras and manpower to monitor the live feed 24/7, room for missing important information due to human error exists. In addition to that, employing an adequate number of people for the job is not always feasible either. The solution lies in a system that allows automated surveillance through classification and other data mining techniques that can be used for extraction of useful information out of these inputs. In this research, a data mining-based framework has been proposed for surveillance. The research includes interpretation of data from different networks using hybrid data mining technique. In order to show the validity of the proposed hybrid data mining technique, an online data set containing network of a suspicious group has been utilized and main leaders of network has been identified.

Suggested Citation

  • Fouzi Harrag & Ali Alshehri, 2023. "Applying Data Mining in Surveillance: Detecting Suspicious Activity on Social Networks," International Journal of Distributed Systems and Technologies (IJDST), IGI Global, vol. 14(1), pages 1-24, January.
    • Handle: RePEc:igg:jdst00:v:14:y:2023:i:1:p:1-24
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    References listed on IDEAS

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    1. Erico N de Souza & Kristina Boerder & Stan Matwin & Boris Worm, 2016. "Improving Fishing Pattern Detection from Satellite AIS Using Data Mining and Machine Learning," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-20, July.
    2. Stephen P. Borgatti, 2006. "Identifying sets of key players in a social network," Computational and Mathematical Organization Theory, Springer, vol. 12(1), pages 21-34, April.
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