Kevin Chai

Curtin University, A*STAR Graduate Academy

Papers

Total Citations

H-Index

About

Kevin Chai’s research spans cybersecurity and hardware acceleration, with a focus on detecting malicious web robots and advancing energy-efficient computing. His early work pioneered behaviour-based web spambot detection, demonstrating that spambots could be identified by analyzing navigation patterns, action timing, and frequency—rather than relying on static signatures. His 2010 paper on this topic has garnered 39 citations, establishing a foundation for adaptive bot detection in Web 2.0 environments. Chai’s subsequent research refined these techniques, achieving 25 citations for his work on action-based metrics. More recently, he has contributed to hardware design, co-authoring a 2021 study on a 32x32 time-domain wavefront accelerator for path planning and scientific simulations—a project that highlights his shift toward high-throughput, low-power computing solutions for robotics and autonomous navigation. This work, though newer, underscores his versatility in tackling both software-based security challenges and hardware-level performance bottlenecks. Chai’s career reflects a commitment to solving real-world problems, from combating spam to enabling efficient robot control, making his research relevant to students and practitioners in cybersecurity, robotics, and computer architecture.