Detection of Malicious Social Bots Using Learning Automata with URL Features in Twitter Network

Authors

  • M Nithin Department of Artificial Intelligence and Machine Learning, Dr K V Subba Reddy Institute of Technology, Kurnool, Andhra Pradesh, India Author
  • Meer Eshak Ahammad Department of Artificial Intelligence and Machine Learning, Dr K V Subba Reddy Institute of Technology, Kurnool, Andhra Pradesh, India Author
  • Nichenametla Shashank Department of Artificial Intelligence and Machine Learning, Dr K V Subba Reddy Institute of Technology, Kurnool, Andhra Pradesh, India Author
  • Shaik Hyder Ali Department of Artificial Intelligence and Machine Learning, Dr K V Subba Reddy Institute of Technology, Kurnool, Andhra Pradesh, India Author
  • K.Mudduswamy Department of Artificial Intelligence and Machine Learning, Dr K V Subba Reddy Institute of Technology, Kurnool, Andhra Pradesh, India Author

DOI:

https://doi.org/10.32628/CSEIT2511317

Abstract

With the rapid growth of social media platforms like Twitter, malicious social bots have become a significant threat, capable of manipulating public opinion, spreading misinformation, and launching cyber-attacks. These bots often mimic human behavior, making their detection a challenging task. This project proposes a novel approach to detect malicious social bots on Twitter by leveraging Learning Automata in combination with URL-based features extracted from user-generated content. The methodology involves analyzing embedded URLs in tweets—such as domain reputation, frequency, and redirection patterns—to identify abnormal behavior commonly associated with bot activity. Learning Automata, a reinforcement learning technique, is employed to adaptively improve the classification of accounts as benign or malicious over time. The proposed model is trained and evaluated on real-world Twitter datasets, demonstrating improved accuracy and adaptability compared to traditional machine learning classifiers. This work contributes to the ongoing efforts in social media security by offering a dynamic, scalable, and feature-rich solution for identifying and mitigating the threat posed by malicious social bots.

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Published

08-05-2025

Issue

Section

Research Articles

How to Cite

[1]
M Nithin, Meer Eshak Ahammad, Nichenametla Shashank, Shaik Hyder Ali, and K.Mudduswamy, “Detection of Malicious Social Bots Using Learning Automata with URL Features in Twitter Network”, Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, vol. 11, no. 3, pp. 261–266, May 2025, doi: 10.32628/CSEIT2511317.