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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Posts
如何配置Anaconda+PyTorch+Spyder机器学习环境
Published:
通过这篇博文，教你手把手配置好一套方便常用的机器学习/深度学习环境（附CUDA+PyTorch-gpu版配置方法）
我的考研记录
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“读圣贤书，所学何事？而今而后，庶几无愧。”
我的奋斗观
Published:
“踏石有印，抓铁有痕。”
交响乐杂谈（一） 罗西尼《威廉·退尔序曲》
Published:
“尽管前途漫长，处境艰辛，但我们更要竭力结束这无尽邪恶，让和平降临家园。”
我眼中的魔都
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“在上海汇聚了世界各地的人。各人过着各人的生活。每一位都是平等没有差别的。这是一个所谓的‘国际都市’。寻遍全世界，还没有一像上海那样的都市。” ——村松梢风
序
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“天高地迥，觉宇宙之无穷；兴尽悲来，识盈虚之有数。” ——《滕王阁序》王勃
portfolio
Portfolio item number 1
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Short description of portfolio item number 1
Portfolio item number 2
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Short description of portfolio item number 2
publications
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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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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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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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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talks
Talk 1 on Relevant Topic in Your Field
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This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!
Conference Proceeding talk 3 on Relevant Topic in Your Field
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This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
This is a description of a teaching experience. You can use markdown like any other post.
Teaching experience 2
Workshop, University 1, Department, 2015
This is a description of a teaching experience. You can use markdown like any other post.