Young‐Gab Kim

Papers

Total Citations

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About

Young-Gab Kim is a leading researcher in cybersecurity and web intelligence, with a particular focus on web robot detection and secure system design. His most-cited work, "Web robot detection based on pattern-matching technique" (2012, 25 citations), addresses a critical challenge in web security: distinguishing automated bots from human users. Kim’s key contribution lies in advancing beyond simplistic detection features—such as empty referrer fields or request intervals—by developing sophisticated pattern-matching methodologies that capture the diverse behavioral characteristics of modern web robots. This work has significant implications for protecting web servers from malicious scraping, DDoS attacks, and fraudulent traffic. Beyond this foundational paper, Kim has made notable contributions to authentication protocols, IoT security, and blockchain-based systems, consistently publishing in high-impact venues. His research is characterized by a practical, problem-driven approach that bridges theoretical security models with real-world deployment challenges. With a growing citation footprint and ongoing work in AI-driven security analytics, Young-Gab Kim continues to shape how we understand and defend against automated threats in the digital ecosystem.