no code implementations • 19 Oct 2021 • Bo Pang, Yongquan Fu, Siyuan Ren, Ye Wang, Qing Liao, Yan Jia
Extensive evaluation over real-world traffic data sets, including normal, encrypted and malicious labels, show that, CGNN improves the prediction accuracy by 23\% to 29\% for application classification, by 2\% to 37\% for malicious traffic classification, and reaches the same accuracy level for encrypted traffic classification.
no code implementations • 18 Aug 2021 • Siyuan Ren, Bin Guo, Longbing Cao, Ke Li, Jiaqi Liu, Zhiwen Yu
To address these issues, we propose DeepExpress - a deep-learning based express delivery sequence prediction model, which extends the classic seq2seq framework to learning complex coupling between sequence and features.