A survey on metaverse: Fundamentals, security, and privacy

Y Wang, Z Su, N Zhang, R **ng, D Liu… - … surveys & tutorials, 2022 - ieeexplore.ieee.org
Metaverse, as an evolving paradigm of the next-generation Internet, aims to build a fully
immersive, hyper spatiotemporal, and self-sustaining virtual shared space for humans to …

Cyber risk and cybersecurity: a systematic review of data availability

F Cremer, B Sheehan, M Fortmann… - The Geneva papers …, 2022 - pmc.ncbi.nlm.nih.gov
Cybercrime is estimated to have cost the global economy just under USD 1 trillion in 2020,
indicating an increase of more than 50% since 2018. With the average cyber insurance …

Hierarchical adversarial attacks against graph-neural-network-based IoT network intrusion detection system

X Zhou, W Liang, W Li, K Yan, S Shimizu… - IEEE Internet of …, 2021 - ieeexplore.ieee.org
The advancement of Internet of Things (IoT) technologies leads to a wide penetration and
large-scale deployment of IoT systems across an entire city or even country. While IoT …

A novel scenarios engineering methodology for foundation models in metaverse

X Li, Y Tian, P Ye, H Duan… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Foundation models are used to train a broad system of general data to build adaptations to
new bottlenecks. Typically, they contain hundreds of billions of hyperparameters that have …

Meta-learning as a promising approach for few-shot cross-domain fault diagnosis: Algorithms, applications, and prospects

Y Feng, J Chen, J **e, T Zhang, H Lv, T Pan - Knowledge-Based Systems, 2022 - Elsevier
The advances of intelligent fault diagnosis in recent years show that deep learning has
strong capability of automatic feature extraction and accurate identification for fault signals …

Comparative research on network intrusion detection methods based on machine learning

C Zhang, D Jia, L Wang, W Wang, F Liu, A Yang - Computers & Security, 2022 - Elsevier
Network intrusion detection system is an essential part of network security research. It
detects intrusion behaviors through active defense technology and takes emergency …

Distribution bias aware collaborative generative adversarial network for imbalanced deep learning in industrial IoT

X Zhou, Y Hu, J Wu, W Liang, J Ma… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
The impact of Internet of Things (IoT) has become increasingly significant in smart
manufacturing, while deep generative model (DGM) is viewed as a promising learning …

[HTML][HTML] Network intrusion detection model based on CNN and GRU

B Cao, C Li, Y Song, Y Qin, C Chen - Applied Sciences, 2022 - mdpi.com
A network intrusion detection model that fuses a convolutional neural network and a gated
recurrent unit is proposed to address the problems associated with the low accuracy of …

Fast anomaly identification based on multiaspect data streams for intelligent intrusion detection toward secure industry 4.0

L Qi, Y Yang, X Zhou, W Rafique… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Various cyber attacks often occur in logistics network of the Industry 4.0, which poses a
threat to Internet security. Intrusion detection can intelligently detect anomalous activities …

Dynamic graph convolutional recurrent imputation network for spatiotemporal traffic missing data

X Kong, W Zhou, G Shen, W Zhang, N Liu… - Knowledge-Based …, 2023 - Elsevier
In real-world intelligent transportation systems, the spatiotemporal traffic data collected from
sensors often exhibit missing or corrupted data, significantly hindering the development of …