Yao Du

Title: Yao Du - Innovator in Malicious Code Detection

Introduction

Yao Du is a prominent inventor based in Chengdu, China. He has made significant contributions to the field of cybersecurity, particularly in the detection of malicious code in Android applications. His innovative approach leverages community structure analysis to enhance the efficiency and accuracy of malware detection.

Latest Patents

Yao Du holds a patent for a "Malicious code detection method based on community structure analysis." This invention presents a novel Android malicious code detection method that utilizes community structure analysis. The method begins with the automatic acquisition of critical static feature information from the target program, including permissions, functions, classes, and system APIs. Following this, it constructs a function call graph based on the relationships between functions and preprocesses this graph. The method involves cycle division and analysis of the weighted function call graph to achieve accurate community structure division. Finally, features are extracted from these community structures for machine learning, leading to a definitive determination of maliciousness. This method is particularly effective in analyzing program internal structures and detecting malicious code rapidly, especially when dealing with numerous Android application samples generated by repackaging technology.

Career Highlights

Yao Du is affiliated with Sichuan University, where he continues to advance his research and development in the field of cybersecurity. His work has garnered attention for its practical applications and innovative methodologies.

Collaborations

Yao Du has collaborated with notable colleagues, including Junfeng Wang and Xiaosong Zhang, contributing to the advancement of research in malicious code detection.

Conclusion

Yao Du's contributions to the field of cybersecurity, particularly through his patented method for detecting malicious code, highlight his role as an influential inventor. His work not only addresses current challenges in malware detection but also paves the way for future innovations in the field.