Zhiqi Li

Zhiqi Li

Master student in Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences

Publications

Journal Articles

AI-Enabled Trust in Distributed Networks

Published in IEEE ACCESS, 2023

This review focuses on the concept of trust and how it can be facilitated through AI, particularly utilizing machine learning and deep learning methods. Additionally, the paper provides a comprehensive comparison and analysis of three key domains in the field of AI-enabled trust: trust management (TM), intrusion detection system (IDS), and recommender systems (RS). Some open problems and challenges that currently exist in the field are manifested, and some suggestions for future work are presented.

Recommended citation: Li, Z., Fang, W., Zhu, C., Gao, Z., & Zhang, W. (2023). AI-enabled trust in distributed networks. IEEE Access, 11, 88116-88134.
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Conference Papers

Toward Deep Learning based Intrusion Detection System: A Survey

Published in 2024 6th International Conference on Big Data Engineering (BDE 2024), 2024

To facilitate researchers access to the latest breakthroughs, we delve into recent advancements in DL-based IDS proposed over the past years. These works are systematically categorized into two main application domains: computer networks and the Internet of Things (IoT), and their methodology, accuracy performance, advantages, and disadvantages undergo scrutiny in each work, fostering an insightful comparison. Subsequently, meticulous examination and deliberation are conducted on the shared traits and distinctive features across these works. Drawing from the collective insights gleaned from the reviewed literature, the current developmental landscape is synthesized, and prospective research directions for future works are delineated in the conclusion.

Recommended citation: Li, Z., Fang, W., Zhu, C., Song, G., & Zhang, W. (2024, July). Toward Deep Learning based Intrusion Detection System: A Survey. In Proceedings of the 2024 6th International Conference on Big Data Engineering (pp. 25-32).
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Deep Neural Network-Based Intrusion Detection in Internet of Things: A State-of-the-Art Review

Published in 20th International Conference on Intelligent Computing (ICIC 2024), 2024

Due to the rapid development of machine learning technology in recent years, deep neural networks (DNNs) emerge as powerful models utilized to significantly enhance the accuracy performance of intrusion detection systems (IDSs) and to increase their adaptability to dynamic networks. In this paper, related works in the last three years are collected and selected, considering both traffic-based and behavior-based intrusion detection. Subsequently, a study and analysis of these related works is conducted. Additionally, we compare their techniques utilized, results, advantages, and disadvantages. Finally, we analyze the existing challenges and open issues and suggest some insightful future research works.

Recommended citation: Li, Z., Fang, W., Zhu, C., Chen, W., Gao, Z., Jiang, X., & Zhang, W. (2024, August). Deep Neural Network-Based Intrusion Detection in Internet of Things: A State-of-the-Art Review. In International Conference on Intelligent Computing (pp. 13-23). Singapore: Springer Nature Singapore.
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Trust Evaluation with Deep Learning in Online Social Networks: A State-of-the-Art Review

Published in 20th International Conference on Intelligent Computing (ICIC 2024), 2024

We analyze and compare some recent related research, summarizing prevalent challenges and open issues while proposing optimization strategies to address them. For instance, graph-based neural networks methods often grapple with exponentially increasing computational complexity as network size expands, and imbalanced datasets typically lead to reduced model accuracy and generalization. Lastly, it presents several promising avenues for future research in the field.

Recommended citation: Li, Z., Fang, W., Zhu, C., Chen, W., Hao, T., & Zhang, W. (2024, August). Trust evaluation with deep learning in online social networks: A state-of-the-art review. In International Conference on Intelligent Computing (pp. 3-12). Singapore: Springer Nature Singapore.
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