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Comprehensive Analysis of Online Social Network Frauds

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Advances in Data-Driven Computing and Intelligent Systems (ADCIS 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 698))

Abstract

Over the past few years, the popularity of online social networks (OSN) has been tremendous, and OSN has become a part of everyone’s lives. People uses OSN services to stay connected with each other for social interactions, business opportunities, entertainment, and career. The open nature and popularity of OSN have resulted in a number of frauds and misappropriations. Online Social Network Fraud (OSNF) is a growing threat in cyberspace through OSN sites. The risk is even higher when its targets are adults, children, and females. This chapter focuses on comprehensive analysis of different types of OSN fraud based on the key intention of fraud by describing the nature of the fraud and its potential harm to users, statistics of OSNF and the interrelationship between OSN frauds, threats, and attacks. In addition, we analyzed the most recent and cutting-edge findings from literature for detection of OSNF using machine learning algorithms. Although the machine learning algorithms are capable of detecting the OSNF still there are many challenges due to the complex nature of the OSN.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Sandip University, Nashik, Ramrao Adik Institute of Technology, D. Y. Patil Deemed to be University, Navi Mumbai, India

    Smita Bharne

  2. School of Computer Science and Engineering, Sandip University, Nashik, Navi Mumbai, India

    Pawan Bhaladhare

Authors
  1. Smita Bharne
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  2. Pawan Bhaladhare
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Corresponding author

Correspondence to Smita Bharne .

Editor information

Editors and Affiliations

  1. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, India

    Swagatam Das

  2. Birla Institute of Technology and Science, Goa, India

    Snehanshu Saha

  3. Department of Computer Science, Center for Research and Advanced Studies of the National Polytechnic Institute, Mexico City, Mexico

    Carlos A. Coello Coello

  4. Department of Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

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Bharne, S., Bhaladhare, P. (2023). Comprehensive Analysis of Online Social Network Frauds. In: Das, S., Saha, S., Coello Coello, C.A., Bansal, J.C. (eds) Advances in Data-Driven Computing and Intelligent Systems. ADCIS 2022. Lecture Notes in Networks and Systems, vol 698. Springer, Singapore. https://doi.org/10.1007/978-981-99-3250-4_3

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  • DOI: https://doi.org/10.1007/978-981-99-3250-4_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-3249-8

  • Online ISBN: 978-981-99-3250-4

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