Guorui Xie

About Me

My name is Xie, Guorui (谢国锐 in Chinese). In 2019, I received a B.E. degree from SYSU, China. Now I am a Ph.D. student (2019-2024) at THU, China, also a visiting student at Polimi, Italy.

My main research interest is traffic analysis on programmable network devices, including 1) deploy P4 based ML models for traffic analysis; 2) design the privacy-secured traffic obfuscation system; 3) develop traffic measurement and scheduling system.

I will graduate in 2024.12 and am on the job market now! My resume in English/Chinese.

First-Author Publication

1. Generating neural networks for diverse networking classification tasks via hardware-aware neural architecture search
   G. Xie, Q. Li, Z. Shi, et al.
   IEEE Transactions on Computers, TC2023 (CCF A).

2. Dryad: deploying adaptive trees on programmable switches for networking classification
   G. Xie, Q. Li, J. Lin, et al.
   IEEE International Conference on Network Protocols, ICNP2023 (TsinghuaCS A, CCF B).

3. Efficient attack detection with multi-latency neural models on heterogeneous network devices
   G. Xie, Q. Li, H. Yan, et al.
   IEEE International Conference on Network Protocols, ICNP2023 (TsinghuaCS A, CCF B).

4. Empowering in-network classification in programmable switches by binary decision tree and knowledge distillation
   G. Xie, Q. Li, G. Duan, J. Lin, et al.
   IEEE/ACM Transactions on Networking, ToN2023 (CCF A).

5. Efficient flow recording with InheritSketch on programmable switches
   G. Xie, Q. Li, G. Duan, et al.
   International Conference on Distributed Computing Systems, ICDCS2023 (CCF B).

6. Soter: deep learning enhanced in-network attack detection based on programmable switches
   G. Xie, Q. Li, C. Cui, et al.
   International Symposium on Reliable Distributed Systems, SRDS2022 (CCF B).

7. Mousika: enable general in-network intelligence in programmable switches by knowledge distillation
   G. Xie, Q. Li, Y. Dong, G. Duan, et al.
   International Conference on Computer Communications, INFOCOM2022 (CCF A).

8. Self-attentive deep learning method for online traffic classification and its interpretability
   G. Xie, Q. Li, and Y. Jiang.
   Elsevier Computer Networks, CN2021 (CCF B).

9. SAM: self-attention based deep learning method for online traffic classification
   G. Xie, Q. Li, Y. Jiang, T. Dai, G. Shen, R. Li, R. Sinnott, and S. Xia.
   Workshop on Network Meets AI & ML, NetAI@SIGCOMM, NetAI2020 (CCF A Workshop).