Papers
1
Total Citations
2
H-Index
1
About
Atul Fegade’s research centers on cybersecurity and social media integrity, with a particular focus on detecting malicious social bots. His most cited work, “Detection of Malicious Social Bots with the Aid of Learning Automata on Twitter” (2022), introduces an innovative approach that combines learning automata with machine learning to identify automated accounts that spread propaganda, assume fake identities, or distribute malicious URLs. This contribution addresses a critical challenge in online trust and safety, offering a scalable method to counter disinformation campaigns. While his citation count is still growing, Fegade’s work has been recognized for its practical relevance in an era of rising social media manipulation. His research stands out for bridging adaptive learning algorithms with real-world threat detection, making it valuable for both academic researchers and cybersecurity practitioners. As the fight against automated abuse intensifies, Fegade’s contributions provide a foundation for more resilient social media ecosystems.
Research Focus
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Top Papers
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