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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 systematic literature review of methods and datasets for anomaly-based network intrusion detection
Zhen Yang, Xiaodong Liu, Tong Li, et al.
Computers & Security (2022) Vol. 116, pp. 102675-102675
Open Access | Times Cited: 186

Showing 1-25 of 186 citing articles:

Internet of Things intrusion detection systems: a comprehensive review and future directions
Arash Heidari, Mohammad Ali Jabraeil Jamali
Cluster Computing (2022) Vol. 26, Iss. 6, pp. 3753-3780
Closed Access | Times Cited: 132
A multimodal hybrid parallel network intrusion detection model
Shuxin Shi, Dezhi Han, Mingming Cui
Connection Science (2023) Vol. 35, Iss. 1
Open Access | Times Cited: 95
Cyber Threats Detection in Smart Environments Using SDN-Enabled DNN-LSTM Hybrid Framework
Mohammad Al Razib, Danish Javeed, Muhammad Taimoor Khan, et al.
IEEE Access (2022) Vol. 10, pp. 53015-53026
Open Access | Times Cited: 80
A stacking ensemble of deep learning models for IoT intrusion detection
Riccardo Lazzarini, Huaglory Tianfield, Vassilis Charissis
Knowledge-Based Systems (2023) Vol. 279, pp. 110941-110941
Open Access | Times Cited: 76
A systematic literature review for network intrusion detection system (IDS)
Oluwadamilare Harazeem Abdulganiyu, Taha Ait Tchakoucht, Yakub Kayode Saheed
International Journal of Information Security (2023) Vol. 22, Iss. 5, pp. 1125-1162
Closed Access | Times Cited: 64
Fed-ANIDS: Federated learning for anomaly-based network intrusion detection systems
Meryem Janati Idrissi, Hamza Alami, Abdelkader El Mahdaouy, et al.
Expert Systems with Applications (2023) Vol. 234, pp. 121000-121000
Closed Access | Times Cited: 58
A survey on imbalanced learning: latest research, applications and future directions
Wuxing Chen, Kaixiang Yang, Zhiwen Yu, et al.
Artificial Intelligence Review (2024) Vol. 57, Iss. 6
Open Access | Times Cited: 47
Advancing cybersecurity: a comprehensive review of AI-driven detection techniques
A Salem, Safaa M. Azzam, O. E. Emam, et al.
Journal Of Big Data (2024) Vol. 11, Iss. 1
Open Access | Times Cited: 19
ResInceptNet-SA: A Network Traffic Intrusion Detection Model Fusing Feature Selection and Balanced Datasets
Guorui Liu, Tianlin Zhang, Hualin Dai, et al.
Applied Sciences (2025) Vol. 15, Iss. 2, pp. 956-956
Open Access | Times Cited: 1
On the ICN-IoT with federated learning integration of communication: Concepts, security-privacy issues, applications, and future perspectives
Anichur Rahman, Kamrul Hasan, Dipanjali Kundu, et al.
Future Generation Computer Systems (2022) Vol. 138, pp. 61-88
Closed Access | Times Cited: 66
HC-DTTSVM: A Network Intrusion Detection Method Based on Decision Tree Twin Support Vector Machine and Hierarchical Clustering
Li Zou, Xuemei Luo, Yan Zhang, et al.
IEEE Access (2023) Vol. 11, pp. 21404-21416
Open Access | Times Cited: 32
A novel intelligent approach for man‐in‐the‐middle attacks detection over internet of things environments based on message queuing telemetry transport
Álvaro Michelena, José Aveleira‐Mata, Esteban Jove, et al.
Expert Systems (2023) Vol. 41, Iss. 2
Open Access | Times Cited: 29
An empirical assessment of ensemble methods and traditional machine learning techniques for web-based attack detection in industry 5.0
Oumaima Chakir, Abdeslam Rehaimi, Yassine Sadqi, et al.
Journal of King Saud University - Computer and Information Sciences (2023) Vol. 35, Iss. 3, pp. 103-119
Open Access | Times Cited: 28
MalBoT-DRL: Malware Botnet Detection Using Deep Reinforcement Learning in IoT Networks
Mohammad Al-Fawa’reh, Jumana Abu-Khalaf, Patryk Szewczyk, et al.
IEEE Internet of Things Journal (2023) Vol. 11, Iss. 6, pp. 9610-9629
Open Access | Times Cited: 26
Towards an efficient model for network intrusion detection system (IDS): systematic literature review
Oluwadamilare Harazeem Abdulganiyu, Taha Ait Tchakoucht, Yakub Kayode Saheed
Wireless Networks (2023) Vol. 30, Iss. 1, pp. 453-482
Closed Access | Times Cited: 22
A Review of Generative Models in Generating Synthetic Attack Data for Cybersecurity
Garima Agrawal, Amardeep Kaur, Sowmya Myneni
Electronics (2024) Vol. 13, Iss. 2, pp. 322-322
Open Access | Times Cited: 14
Real-time fusion multi-tier DNN-based collaborative IDPS with complementary features for secure UAV-enabled 6G networks
Hassan Jalil Hadi, Yue Cao, Sifan Li, et al.
Expert Systems with Applications (2024) Vol. 252, pp. 124215-124215
Closed Access | Times Cited: 10
Overview on Intrusion Detection Systems for Computers Networking Security
Lorenzo Diana, Pierpaolo Dini, Davide Paolini
Computers (2025) Vol. 14, Iss. 3, pp. 87-87
Open Access | Times Cited: 1
MEMBER: A multi-task learning model with hybrid deep features for network intrusion detection
Jinghong Lan, Xudong Liu, Bo Li, et al.
Computers & Security (2022) Vol. 123, pp. 102919-102919
Closed Access | Times Cited: 28
Stochastic gradient descent classifier-based lightweight intrusion detection systems using the efficient feature subsets of datasets
Jahongir Azimjonov, Taehong Kim
Expert Systems with Applications (2023) Vol. 237, pp. 121493-121493
Closed Access | Times Cited: 21
Ensemble-Based Deep Learning Models for Enhancing IoT Intrusion Detection
Ammar Odeh, Anas Abu Taleb
Applied Sciences (2023) Vol. 13, Iss. 21, pp. 11985-11985
Open Access | Times Cited: 19
Federated learning for reliable model updates in network-based intrusion detection
Roger R. dos Santos, Eduardo K. Viegas, Altair O. Santin, et al.
Computers & Security (2023) Vol. 133, pp. 103413-103413
Closed Access | Times Cited: 17
The cybersecurity mesh: A comprehensive survey of involved artificial intelligence methods, cryptographic protocols and challenges for future research
Bruno Ramos-Cruz, Javier Andreu-Pérez, Luis Martı́nez
Neurocomputing (2024) Vol. 581, pp. 127427-127427
Open Access | Times Cited: 6
Overcoming the Challenges of Data Lack, Leakage, and Dimensionality in Intrusion Detection Systems: A Comprehensive Review
Mohamed Aly Bouke, Azizol Abdullah, Nur Izura Udzir, et al.
Journal of Communication and Information Systems (2024) Vol. 39, Iss. 2024, pp. 22-34
Open Access | Times Cited: 5
Anomaly and intrusion detection using deep learning for software-defined networks: A survey
Vitor Gabriel da Silva Ruffo, Daniel Matheus Brandão Lent, Mateus Komarchesqui, et al.
Expert Systems with Applications (2024) Vol. 256, pp. 124982-124982
Closed Access | Times Cited: 5
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