3 citations · 3 across the 4 of their papers we have counts for
5 papers
Tanchun: Robust Flow-Based HTTP(S) DDoS Detection via Behavioral Attribute Guidance
Qingyang Li, Yunpeng Tan, Mingxin Yang +5
HTTP(S) DDoS attacks have surged in frequency and scale, posing a significant threat to network security. Flow-based detection is a promising solution for large-scale HTTP(S) DDoS…
MalMoE: Mixture-of-Experts Enhanced Encrypted Malicious Traffic Detection Under Graph Drift
Yunpeng Tan, Qingyang Li, Mingxin Yang +3
Encryption has been commonly used in network traffic to secure transmission, but it also brings challenges for malicious traffic detection, due to the invisibility of the packet pa…
NetMamba+: A Framework of Pre-trained Models for Efficient and Accurate Network Traffic Classification
Tongze Wang, Xiaohui Xie, Wenduo Wang +6
With the rapid growth of encrypted network traffic, effective traffic classification has become essential for network security and quality of service management. Current machine le…
LLM-Sketch: Enhancing Network Sketches with LLM
Yuanpeng Li, Zhen Xu, Zongwei Lv +3
Network stream mining is fundamental to many network operations. Sketches, as compact data structures that offer low memory overhead with bounded accuracy, have emerged as a promis…
DoLLM: How Large Language Models Understanding Network Flow Data to Detect Carpet Bombing DDoS
Qingyang Li, Yihang Zhang, Zhidong Jia +7
It is an interesting question Can and How Large Language Models (LLMs) understand non-language network data, and help us detect unknown malicious flows. This paper takes Carpet Bom…