Papers
2
Total Citations
17
H-Index
2
About
Yu Guo is a researcher whose work spans two distinct but equally impactful domains: cybersecurity and computer vision. In the realm of network security, Guo has made notable contributions to the detection of malicious automated web traffic, developing machine learning-based approaches to identify CloudBots through multi-layer traffic statistical analysis. This work addresses critical threats to e-commerce ecosystems, including click fraud and fake account registration — problems with significant real-world economic consequences. On the computer vision front, Guo has tackled the challenging problem of simultaneous image deblurring and super-resolution, proposing a deep dual-branch network architecture that enhances image quality for mobile and robotic vision applications. This research addresses the practical limitations of mobile cameras, where motion blur frequently degrades image quality and undermines downstream processing tasks. With publications accumulating citations across both security and vision communities, Guo demonstrates a versatile research profile that bridges applied machine learning methodologies across multiple disciplines. His work reflects a commitment to solving practical, real-world problems through innovative deep learning and statistical modeling techniques.
Research Focus
Key Achievements
Top Papers
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