JZUS - Journal of Zhejiang University SCIENCE

Frontiers of Information Technology & Electronic Engineering

Accepted manuscript available online (unedited version)

FedSTGCN: a novel federated spatiotemporal graph learning-based network intrusion detection for the Internet of Things

Author(s): Yalu WANG¹, Jie LI², Zhijie HAN³, Pu CHENG³, Roshan KUMAR⁴
Affiliation(s): ¹School of Computer and Information Engineering, Henan University, Kaifeng 475004, China; more
Corresponding email(s): hanzj@henu.edu.cn
Key Words: Internet of Things; Network intrusion detection, Spatiotemporal graph neural network; Federated learning; Data privacy.

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Yalu WANG¹, Jie LI², Zhijie HAN³, Pu CHENG³, Roshan KUMAR⁴. FedSTGCN: a novel federated spatiotemporal graph learning-based network intrusion detection for the Internet of Things[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2400932

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author="Yalu WANG¹, Jie LI², Zhijie HAN³, Pu CHENG³, Roshan KUMAR⁴",
journal="Frontiers of Information Technology & Electronic Engineering",
year="in press",
publisher="Zhejiang University Press & Springer",
doi="https://doi.org/10.1631/FITEE.2400932"
}

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%A Pu CHENG³
%A Roshan KUMAR⁴
%J Frontiers of Information Technology & Electronic Engineering
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doi="https://doi.org/10.1631/FITEE.2400932"

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A1 - Pu CHENG³
A1 - Roshan KUMAR⁴
J0 - Frontiers of Information Technology & Electronic Engineering
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Abstract
Chinese Summary
Academic Network
Reviewer Comment

Abstract: Image generation models have made remarkable progress, and image evaluation is crucial for explaining and driving the development of these models. Previous studies have extensively explored human and automatic evaluations of image generation. Herein, these studies are comprehensively surveyed, specifically for two main parts: evaluation protocols and evaluation methods. First, 10 image generation tasks are summarized by focusing on their differences in evaluation aspects. Based on this, a novel protocol is proposed to cover human and automatic evaluation aspects required for various image generation tasks. Second, the review of automatic evaluation methods in the past five years is highlighted. To our knowledge, this paper presents the first comprehensive summary of human evaluation, encompassing evaluation methods, tools, details, and data analysis methods. Finally, this the challenges and potential directions for image generation evaluation are discussed. We hope that this survey will help researchers to develop a systematic understanding of image generation evaluation, stay updated with the latest advancements in the field, and encourage further research.

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