1 code implementation • 12 Feb 2024 • Haozhen Zhang, Xi Xiao, Le Yu, Qing Li, Zhen Ling, Ye Zhang
In particular, we utilize supervised contrastive learning to enhance the packet-level and flow-level representations and perform graph data augmentation on the byte-level traffic graph so that the fine-grained semantic-invariant characteristics between bytes can be captured through contrastive learning.
1 code implementation • 31 Jul 2023 • Haozhen Zhang, Le Yu, Xi Xiao, Qing Li, Francesco Mercaldo, Xiapu Luo, Qixu Liu
Encrypted traffic classification is receiving widespread attention from researchers and industrial companies.
1 code implementation • 13 Jun 2023 • Haozhen Zhang, Xueting Han, Xi Xiao, Jing Bai
To address these issues, we propose a Time-aware Graph Structure Learning (TGSL) approach via sequence prediction on temporal graphs, which learns better graph structures for downstream tasks through adding potential temporal edges.
1 code implementation • 10 Dec 2020 • Haozhen Zhang, Wei Zhao, Shuang Liu
The classification of electrocardiogram (ECG) signals, which takes much time and suffers from a high rate of misjudgment, is recognized as an extremely challenging task for cardiologists.