Xiaosheng Liu

Papers

Total Citations

H-Index

About

Xiaosheng Liu is a leading researcher in the cybersecurity of industrial robotic systems, with a focus on vulnerability discovery and automated threat modeling. His most notable contribution is the development of FuzzAGG, a fuzzing-driven attack graph generation framework for industrial robot systems, published in 2024. This work pioneers a novel approach that combines fuzzing techniques with attack graph analysis to systematically uncover and map security weaknesses in complex robotic environments. By automating the generation of attack graphs through fuzzing, Liu’s framework enables researchers and practitioners to identify critical attack paths and prioritize defenses in real-world industrial settings. Though his seminal paper has garnered 3 citations in its first year, its impact is already evident in shaping the intersection of fuzzing and security modeling for cyber-physical systems. Liu’s research addresses the pressing need for robust security assessments in Industry 4.0, where robotic systems are increasingly interconnected and vulnerable. His work stands out for its practical applicability, offering a scalable solution to a previously manual and error-prone process. As a rising voice in industrial cybersecurity, Liu continues to advance the field by bridging theoretical security models with empirical, fuzzing-driven validation.