10 citations · 10 across the 2 of their papers we have counts for
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cs.CR2026
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…
cs.CR2024★ 10 cited
Unveiling the Vulnerability of Private Fine-Tuning in Split-Based Frameworks for Large Language Models: A Bidirectionally Enhanced Attack
Guanzhong Chen, Zhenghan Qin, Mingxin Yang +4
Recent advancements in pre-trained large language models (LLMs) have significantly influenced various domains. Adapting these models for specific tasks often involves fine-tuning (…
cs.CR2024
VulDetectBench: Evaluating the Deep Capability of Vulnerability Detection with Large Language Models
Yu Liu, Lang Gao, Mingxin Yang +4
Large Language Models (LLMs) have training corpora containing large amounts of program code, greatly improving the model's code comprehension and generation capabilities. However,…