IJCT Policy Indexing Low Cost Publication Paper Submission Publication Guidelines

International Journal of Computer Techniques Volume 12 Issue 4 | Machine Learning Techniques for Fake Acount Detection in Social Networks

Machine Learning Techniques for Fake Account Detection in Social Networks | IJCT Journal

Machine Learning Techniques for Fake Account Detection in Social Networks

Authors:
Abdul Muthalib Mohammed, MCA Student
Dr. K. Santhi Sree, Professor
Department of Information Technology, Jawaharlal Nehru Technological University Hyderabad, Telangana, India
Email: abdulmuthallib18@gmail.com, drksanthisree@gmail.com

Journal: International Journal of Computer Techniques – Volume 12 Issue 4
Publication Date: July – August 2025
ISSN: 2394-2231
URL: https://ijctjournal.org/

Abstract

This paper explores the use of machine learning models to detect fake profiles in online social networks. Models including Random Forest, Naïve Bayes, SVM, KNN, and Logistic Regression were evaluated using accuracy, precision, recall, and F1-score. Random Forest achieved the highest accuracy of 97.5%, followed closely by Naïve Bayes at 97%. The study highlights the effectiveness of ensemble and probabilistic classifiers and suggests future research in deep learning and behavioral analysis for real-time detection.

Keywords

Fake Profile Detection, Social Networks, Machine Learning, Random Forest, Naïve Bayes, SVM, KNN, Logistic Regression, Cybersecurity, Online Identity Fraud

Conclusion

Among the evaluated models, Random Forest and Naïve Bayes emerged as the most effective for fake account detection. The study confirms the reliability of ensemble and probabilistic approaches in identifying malicious profiles. Future research should explore deep learning, real-time detection, and behavioral modeling to enhance adaptability and precision.

References

  1. Sarah Khaled et al., IEEE Big Data, 2018.
  2. Ala’ M. Al-Zoubi et al., ICICS, 2017.
  3. Adikari & Dutta, PACIS, 2014.
  4. Qahtani & Najjar, ICAICV, 2018.
  5. R. Malik et al., IP&M, 2020.
  6. Gaitonde & Doye, ICICCS, 2019.
  7. S. Kumar & R. Kumar, IJIT, 2021.
  8. Patel & S. Patel, IJCA, 2021.
  9. N. Jain et al., IJARCS, 2021.
  10. M. Baskar et al., IJARCS, 2021.
  11. Agarwal & Tripathi, IJSER, 2020.
  12. Almansour & Alhothali, JISA, 2022.
  13. W. Shu et al., ACM TKDD, 2020.
  14. Y. Zhang & X. Zhang, IEEE TCSS, 2019.
  15. T. Nguyen et al., IJMLC, 2021.
  16. R. Gupta & S. Sharma, IJDSA, 2021.
  17. M. Hussain et al., NCA, 2020.
  18. K. Wang et al., ESWA, 2019.
  19. J. Lee & H. Kim, IEEE Access, 2020.
Machine Learning Techniques for Fake Account Detection in Social NetworksDownload
Tag

IJCT Latest News

Post Comment

Reviewer Board