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Popularity-Based Detection of Malicious Content in Facebook Using Machine Learning Approach

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First International Conference on Sustainable Technologies for Computational Intelligence

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1045))

Abstract

In this world, people are encircled with various online social networks (OSNs) or media platform, various websites and applications. This brings media contents like texts, audio, and videos in daily basis. People share their current status and moments with their belongings to keep in touch by using these tools and software like Twitter, Facebook, and Instagram. The flow of information available in these social networks attract the cybercriminals who misuse this information to exploit vulnerabilities for their illicit benefits such as stealing personal information, advertising some product, attract victims, and infecting user personal system. In this paper, we proposed a popularity-based method which uses PSO-based feature selections and machine learning classifiers to analyze the characteristics of different features for spammer detection in Facebook. Our detection framework result shows higher rate of detection as compared to other techniques.

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Acknowledgements

This research work is funded by Ministry of Electronics & Information Technology, Government of India under YFRF scheme and under the project Visvesvaraya Ph.D. Scheme which is implemented by Digital India Corporation.

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Authors and Affiliations

  1. Department of Computer Engineering, National Institute of Technology, Kurukshetra, Kurukshetra, Haryana, India

    Somya Ranjan Sahoo & B. B. Gupta

Authors
  1. Somya Ranjan Sahoo
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  2. B. B. Gupta
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Corresponding author

Correspondence to B. B. Gupta .

Editor information

Editors and Affiliations

  1. Department of Electrical and Communication Engineering, The Papua New Guinea University of Technology, Lae, Papua New Guinea

    Ashish Kumar Luhach

  2. Neumann János University, Kecskemét, Bács-Kiskun, Hungary

    Janos Arpad Kosa

  3. Amity University, Jaipur, Rajasthan, India

    Ramesh Chandra Poonia

  4. School of Computing, University of Eastern Finland, Kuopio, Finland

    Xiao-Zhi Gao

  5. Department of Computer Science, Namibia University of Science and Technology, Windhoek, Namibia

    Dharm Singh

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Sahoo, S.R., Gupta, B.B. (2020). Popularity-Based Detection of Malicious Content in Facebook Using Machine Learning Approach. In: Luhach, A., Kosa, J., Poonia, R., Gao, XZ., Singh, D. (eds) First International Conference on Sustainable Technologies for Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1045. Springer, Singapore. https://doi.org/10.1007/978-981-15-0029-9_13

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