Rikhi Ram Jagat
Papers
2
Total Citations
10
H-Index
2
About
Rikhi Ram Jagat is a researcher specializing in cybersecurity and machine learning, with a focused interest in detecting malicious web robots. His work addresses the critical challenge of distinguishing between legitimate human users and automated bots in web server logs, a problem of growing importance in the age of data-driven analytics and cyber threats. Jagat’s major contributions include the development of semi-supervised learning models that leverage limited labeled data to improve detection accuracy. His 2022 paper, "Semi-Supervised Self-Training Approach for Web Robots Activity Detection in Weblog," introduced a novel self-training framework that iteratively refines its predictions, achieving robust performance with minimal supervision. Building on this, his 2023 work, "Web-S4AE: a semi-supervised stacked sparse autoencoder model for web robot detection," advanced the field by employing deep learning architectures to capture complex patterns in web traffic. Both papers have garnered 5 citations each, reflecting their early impact in a niche but vital area. Jagat’s research is particularly notable for its practical applicability, offering scalable solutions for real-world web security systems. His work stands as a promising foundation for future studies in adversarial bot detection and anomaly-based cybersecurity.
Research Focus
Key Achievements
Top Papers
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- 2