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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A novel hierarchical attention-based triplet network with unsupervised domain adaptation for network intrusion detection
Jinghong Lan, Xudong Liu, Bo Li, et al.
Applied Intelligence (2022) Vol. 53, Iss. 10, pp. 11705-11726
Closed Access | Times Cited: 12

Showing 12 citing articles:

An Attention-Based Convolutional Neural Network for Intrusion Detection Model
Zhen Wang, Fuad A. Ghaleb
IEEE Access (2023) Vol. 11, pp. 43116-43127
Open Access | Times Cited: 14
ADIMPL: a dynamic, real-time and robustness attack detection model for industrial cyber-physical systems based on improved meta pseudo labels
Bohan Zhang, Pan Zhang, Zhiwen Wang, et al.
Applied Intelligence (2025) Vol. 55, Iss. 6
Closed Access
A Novel and Efficient Multi-scale Spatio-Temporal Residual Network for Multi-class Intrusion Detection
Nan Li, Ziyou Gao, Jiabin Ye, et al.
Lecture notes in computer science (2025), pp. 271-283
Closed Access
A cutting-edge framework for industrial intrusion detection: Privacy-preserving, cost-friendly, and powered by federated learning
Lingzi Zhu, Bo Zhao, Jiabao Guo, et al.
Applied Intelligence (2025) Vol. 55, Iss. 7
Closed Access
A Holistic review and performance evaluation of unsupervised learning methods for network anomaly detection
Niharika Sharma, Bhavna Arora, Shabana R. Ziyad, et al.
International Journal on Smart Sensing and Intelligent Systems (2024) Vol. 17, Iss. 1
Open Access | Times Cited: 2
An Effective Method for Detecting Unknown Types of Attacks Based on Log-Cosh Variational Autoencoder
Yu Li, Liuquan Xu, Xuefeng Jiang
Applied Sciences (2023) Vol. 13, Iss. 22, pp. 12492-12492
Open Access | Times Cited: 4
Multi-source refined adversarial domain adaptation with transfer complementarity infusion for IoT intrusion detection under limited samples
Kehong Li, Wengang Ma, Duan Huawei, et al.
Expert Systems with Applications (2024) Vol. 254, pp. 124352-124352
Closed Access | Times Cited: 1
An Autoencoder-Based Hybrid Detection Model for Intrusion Detection With Small-Sample Problem
Nan Wei, Lihua Yin, Jing-Yi Tan, et al.
IEEE Transactions on Network and Service Management (2023) Vol. 21, Iss. 2, pp. 2402-2412
Closed Access | Times Cited: 3
An Intrusion Detection Method for Industrial Internet Fusing Multi-Scale TCN and Transformer Network
Zhihua Liu, Shenquan Liu, Jian Zhang
Lecture notes in computer science (2024), pp. 82-96
Closed Access
An Effective Method for Detecting Unknown Types of Attacks Based on Log-Cosh Variational Autoencoder
Yu Li, Liuquan Xu, Xuefeng Jiang
(2023)
Open Access | Times Cited: 1
MATTER: A Multi-Level Attention-Enhanced Representation Learning Model for Network Intrusion Detection
Jinghong Lan, Yanan Li, Bo Li, et al.
(2022)
Closed Access | Times Cited: 1
A Malicious Traffic Detection with Domain Adaption in Iot Network
Jia Wang, Mingliang Xiong, Ziyang Wang
(2023)
Closed Access
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