3 citations · 4 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…
PLAA: Packet-level Adversarial Attacks in Network Traffic Detection
Jinhao You, Zan Zhou, Shujie Yang +3
Deep neural networks (DNNs) are widely applied in Network-based Intrusion Detection System (NIDS) due to their high accuracy. However, DNNs are highly susceptible to adversarial at…
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…
Large Language Models powered Malicious Traffic Detection: Architecture, Opportunities and Case Study
Xinggong Zhang, Haotian Meng, Qingyang Li +2
Malicious traffic detection is a pivotal technology for network security to identify abnormal network traffic and detect network attacks. Large Language Models (LLMs) are trained o…
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…