Yonghui Huang

Beijing University of Posts and Telecommunications

Papers

Total Citations

H-Index

About

Yonghui Huang is a researcher at the intersection of cybersecurity and robotics, with a primary focus on securing autonomous systems against evolving digital threats. His most cited work, "MMPD: A Novel Malicious PDF File Detector for Mobile Robots" (2020, 8 citations), addresses a critical vulnerability in human-robot interaction: the use of PDF files—a cross-platform format that can embed malicious JavaScript or URIs—as attack vectors against mobile robots. Huang’s key contribution lies in developing a specialized detection framework that protects robotic platforms from PDF-borne malware, a previously underexplored area in robotics security. By bridging the gap between traditional document security and autonomous system defense, his work highlights the unique risks robots face when sharing files with humans. While his citation count is modest, the novelty of his approach—applying malware detection to robotic document handling—marks an important step in hardening human-robot collaboration. Huang’s research is particularly relevant for students and engineers working on secure robotic deployments in public or industrial settings, where document exchange is common. His work underscores the need for cross-disciplinary solutions that anticipate how hackers exploit everyday tools in emerging technologies.