Xiaoya Ni

Title: Innovations of Xiaoya Ni in Encrypted Traffic Classification

Introduction

Xiaoya Ni is an accomplished inventor based in Guangdong, China. He has made significant contributions to the field of network security, particularly in the classification of encrypted traffic. His innovative approach utilizes machine learning to enhance the identification of normal and malicious traffic.

Latest Patents

Xiaoya Ni holds a patent for a "System for classifying encrypted traffic based on data packet." This system includes a traffic capture module, a traffic analysis module, and a traffic classification module. It collects data packets from a network flow to construct a machine learning model, enabling the classification of encrypted traffic. The system effectively identifies normal and malicious traffic by obtaining various features, including basic spatial-temporal features, header features, load features, and statistical features. Additionally, it focuses on differences between various versions of encryption protocols, particularly the transport layer security (TLS) protocol, to improve the efficiency of traffic classification.

Career Highlights

Xiaoya Ni is affiliated with Guangzhou University, where he continues to advance his research in network security and machine learning. His work has garnered attention for its practical applications in enhancing cybersecurity measures.

Collaborations

Xiaoya Ni collaborates with notable colleagues, including Jing Qiu and Jie Ding, who contribute to his research endeavors.

Conclusion

Xiaoya Ni's innovative work in classifying encrypted traffic represents a significant advancement in network security. His contributions are vital for improving the efficiency of identifying malicious activities in encrypted communications.