An Effective Method for Detection of Untruthful Interpretations in Public Networks

  • Unique Paper ID: 154565
  • Volume: 8
  • Issue: 11
  • PageNo: 528-532
  • Abstract:
  • Social Networks (SNs) have become indispensable parts of our daily lives in recent years, and their popularity is growing at an incredible rate. However, in addition to the revolution that Social Networks have wrought in community interaction, they have also posed a slew of challenges, one of which is the difficulty of categorizing bogus factions as humanoid, bot, or cyborg. We need a system that can detect the most recent social engineering attacks and help the study of live Twitter data. The system application that will be built can also be made available for use, and the data will be stored and created on the server. Such a system's design must protect user privacy, be user-friendly, and detect account misconduct. Furthermore, the detecting system should assign a score to the false user so that they can determine their level of genuineness, as well as allow a legitimate user to erase spam from their profile.

Copyright & License

Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{154565,
        author = {KRISHNAIAH BOYANA and Dr.G.Venkateswara Rao and Mr.K.Bhaskara Rao and Mr.P.Ratna Prakash},
        title = {An Effective Method for Detection of Untruthful Interpretations in Public Networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {11},
        pages = {528-532},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=154565},
        abstract = {Social Networks (SNs) have become indispensable parts of our daily lives in recent years, and their popularity is growing at an incredible rate. However, in addition to the revolution that Social Networks have wrought in community interaction, they have also posed a slew of challenges, one of which is the difficulty of categorizing bogus factions as humanoid, bot, or cyborg. We need a system that can detect the most recent social engineering attacks and help the study of live Twitter data. The system application that will be built can also be made available for use, and the data will be stored and created on the server. Such a system's design must protect user privacy, be user-friendly, and detect account misconduct. Furthermore, the detecting system should assign a score to the false user so that they can determine their level of genuineness, as well as allow a legitimate user to erase spam from their profile.},
        keywords = {Social Networks, TPR (True +Ve rate) and FPR (False +Ve rate), Uniform Resource locator },
        month = {},
        }

Cite This Article

  • ISSN: 2349-6002
  • Volume: 8
  • Issue: 11
  • PageNo: 528-532

An Effective Method for Detection of Untruthful Interpretations in Public Networks

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