报告题目：基于资源受限环境下物联网网络入侵检测模型

报 告 人：李建庆

报告时间：5月14日 下午15:00-16:00

报告地点：景顺铂悦城10幢B座708室（校企合作产业基地）

报告摘要：

With the rapid proliferation of Internet of Things (IoT) devices, cybersecurity has become an increasingly critical concern. However, traditional network intrusion detection methods typically rely on large amounts of high-quality labeled data, a condition that is often difficult to meet in real-world IoT scenarios. To address this challenge, this presentation focuses on effective network intrusion detection in IoT environments under complex conditions, including limited labeled data, unlabeled data, and varying traffic patterns. Our team has proposed several innovative detection mechanisms aimed at deeply exploring and fully utilizing the latent information within network traffic, thereby significantly improving detection performance in scenarios with scarce or even no labels. Experimental results demonstrate that the proposed approaches achieve high detection accuracy and low false alarm rates across diverse network environments, exhibiting strong generalization capability and practical applicability, particularly for IoT environments with limited resources.

报告人简介：

Jianqing Li from the School of Computer Science and Engineering has 274 achievements and collaborates with 594 individuals. His works have been cited 8640 times in the WOS core collection, with an H Index of 46. He has 198 publications in WOS, 29 in CNKI, and 223 in SCOPUS. Comprising 251 papers, 2 books, and 21 patents, his research areas cover the Internet of Things, optical networks, wireless networks, optical fiber sensors, network security, machine learning and its applications, as well as river basin digital twinning.