MIM: A multiple integration model for intrusion detection on imbalanced samples-Reference-Cited by-同舟云学术

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MIM: A multiple integration model for intrusion detection on imbalanced samples

Published:2024-07 Issue:4 Volume:27 Page:
ISSN:1386-145X
Container-title:World Wide Web
language:en
Short-container-title:World Wide Web

Author:

Zhang Zhiqiang,Wang Le,Zhu Junyi,Zhu Dong,Gu Zhaoquan,Zhang Yanchun

Funder

Guangdong Basic andApplied Basic Research Foundation

Major Key Project of PCL

Guangdong High-level University Foundation Program

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11280-024-01285-0.pdf

Reference52 articles.

1. Yan, J., Zhaoquan, G., Zhihao, J., Cuiyun, G., Jianye, Y.: Persistent graph stream summarization for real-time graph analytics. World Wide Web 26, 2647–2667 (2023)

2. Uno, F., Jianxin, L., Naveed, A., Man, L., Yan, J.: GoMIC: Multi-view image clustering via self-supervised contrastive heterogeneous graph co-learning. World Wide Web 26, 1667-1683 (2023)

3. Abhilash, S., Seyed, M.H.M., Jaiprakash, N.: F-TLBO-ID: Fuzzy fed teaching learning based optimisation algorithm to predict the number of k-barriers for intrusion detection. Appl. Soft Comput. 151, 111163 (2024)

4. Bhawana, S., Lokesh, S., Chhagan, L., Satyabrata, R.: Explainable artificial intelligence for intrusion detection in IoT networks: A deep learning based approach. Expert Syst. App. 238, 121751 (2024)

5. Zhiqiang, Z., Le, W., Guangyao, C., Zhaoquan, G., Zhihong, T., Xiaojiang, D., Mohsen, G.: STG2P: A two-stage pipeline model for intrusion detection based on improved LightGBM and K-means. Simul. Model. Pract. Theory 120, 102614 (2022)

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