Jiang Xie

xiejiang@zgclab.edu.cn bitbrave@126.com
+8618222797983
Beijing
I received the B.Eng. from Nankai University, China, in 2018, and the Ph.D. degree from the Institute of Information Engineering, Chinese Academy of Sciences, in 2023. My research interests encompass network security and artificial intelligence, with a particular emphasis on intrusion detection and defense against Advanced Persistent Threats (APTs).
Currently, I am working at Zhongguancun Laboratory in Beijing, where I conduct research in cyberspace security. My primary research areas include:
Intrusion Detection: Developing innovative intrusion detection techniques to enhance the ability to identify network attacks. APT Defense: Analyzing the characteristics and tactics of APT attacks to develop effective defense strategies and technologies. Multi-Step Attack Research and Application: Conducting cutting-edge research on complex multi-step attacks within networks, and focusing on the practical application of these findings to strengthen real-world network security defenses.
selected publications
- (WWW25) AdvTG: An Adversarial Traffic Generation Framework to Deceive DL-Based Malicious Traffic Detection Models2025
- (Infocom2025) FlowMiner: A Powerful Model Based on FlowCorrelation Mining for Encrypted Traffic Classification2025
- Sample analysis and multi-label classification for malicious sample datasetsComput. Networks, 2025
- Let model keep evolving: Incremental learning for encrypted traffic classificationComput. Secur., 2024
- Detecting unknown HTTP-based malicious communication behavior via generated adversarial flows and hierarchical traffic featuresComput. Secur., 2022
- Analysis and Detection against Network Attacks in the Overlapping Phenomenon of Behavior AttributeComput. Secur., 2022
- Analysis and Detection Against Overlapping Phenomenon of Behavioral Attribute in Network AttacksIn Science of Cyber Security - 4th International Conference, SciSec 2022, Matsue, Japan, August 10-12, 2022, Revised Selected Papers, 2022
- HSTF-Model: An HTTP-based Trojan detection model via the Hierarchical Spatio-temporal Features of TrafficsComput. Secur., 2020
- A Method Based on Hierarchical Spatiotemporal Features for Trojan Traffic DetectionIn 38th IEEE International Performance Computing and Communications Conference, IPCCC 2019, London, United Kingdom, October 29-31, 2019, 2019