Divyang Deep Tiwari
Papers
1
Total Citations
12
H-Index
1
About
Divyang Deep Tiwari is a researcher at the forefront of cybersecurity, specializing in the intersection of machine learning and cyber-physical systems (CPS). His work focuses on developing robust, unsupervised learning algorithms to detect sophisticated attacks in critical infrastructure, a domain where traditional signature-based methods often fail. Tiwari’s most-cited paper, "Attack Detection Using Unsupervised Learning Algorithms in Cyber-Physical Systems" (2021), with 12 citations, introduces novel anomaly detection techniques that leverage clustering and dimensionality reduction to identify stealthy intrusions without labeled data. This contribution is pivotal for securing smart grids, autonomous vehicles, and industrial control systems, where real-time, adaptive defenses are essential. Beyond this flagship work, Tiwari’s research explores the broader challenges of adversarial machine learning and the resilience of CPS against evolving threats. His findings have been presented at leading conferences and are increasingly referenced by peers developing next-generation security frameworks. By bridging theoretical advances with practical deployment considerations, Tiwari is helping shape a future where critical systems can autonomously defend against even the most subtle cyberattacks.
Research Focus
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Top Papers
- 1