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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 Comprehensive Survey of Databases and Deep Learning Methods for Cybersecurity and Intrusion Detection Systems
Dilara Gumusbas, Tulay Yldrm, Angelo Genovese, et al.
IEEE Systems Journal (2020) Vol. 15, Iss. 2, pp. 1717-1731
Open Access | Times Cited: 132

Showing 1-25 of 132 citing articles:

A Survey on Machine Learning Techniques for Cyber Security in the Last Decade
Kamran Shaukat, Suhuai Luo, Vijay Varadharajan, et al.
IEEE Access (2020) Vol. 8, pp. 222310-222354
Open Access | Times Cited: 348
Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research
Zhibo Zhang, Hussam Al Hamadi, Ernesto Damiani, et al.
IEEE Access (2022) Vol. 10, pp. 93104-93139
Open Access | Times Cited: 166
Explainable Artificial Intelligence in CyberSecurity: A Survey
Nicola Capuano, Giuseppe Fenza, Vincenzo Loia, et al.
IEEE Access (2022) Vol. 10, pp. 93575-93600
Open Access | Times Cited: 134
Deep learning approaches for detecting DDoS attacks: a systematic review
Meenakshi Mittal, Krishan Kumar, Sunny Behal
Soft Computing (2022) Vol. 27, Iss. 18, pp. 13039-13075
Open Access | Times Cited: 124
Internet of Things (IoT) security dataset evolution: Challenges and future directions
Barjinder Kaur, Sajjad Dadkhah, Farzaneh Shoeleh, et al.
Internet of Things (2023) Vol. 22, pp. 100780-100780
Closed Access | Times Cited: 98
A survey on deep learning for cybersecurity: Progress, challenges, and opportunities
Mayra Macas, Chunming Wu, Walter Fuertes
Computer Networks (2022) Vol. 212, pp. 109032-109032
Closed Access | Times Cited: 92
Reliable Deep Learning and IoT-Based Monitoring System for Secure Computer Numerical Control Machines Against Cyber-Attacks With Experimental Verification
Minh‐Quang Tran, Mahmoud Elsisi, Meng‐Kun Liu, et al.
IEEE Access (2022) Vol. 10, pp. 23186-23197
Open Access | Times Cited: 90
Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review
Wadim Striełkowski, Andrey Vlasov, Kirill Selivanov, et al.
Energies (2023) Vol. 16, Iss. 10, pp. 4025-4025
Open Access | Times Cited: 53
Adversarial examples: A survey of attacks and defenses in deep learning-enabled cybersecurity systems
Mayra Macas, Chunming Wu, Walter Fuertes
Expert Systems with Applications (2023) Vol. 238, pp. 122223-122223
Closed Access | Times Cited: 42
An Intelligent Tree-Based Intrusion Detection Model for Cyber Security
Mohammad Al-Omari, Majdi Rawashdeh, Fadi Qutaishat, et al.
Journal of Network and Systems Management (2021) Vol. 29, Iss. 2
Closed Access | Times Cited: 80
A novel optimized probabilistic neural network approach for intrusion detection and categorization
Nadir Omer, Ahmed H. Samak, Ahmed I. Taloba, et al.
Alexandria Engineering Journal (2023) Vol. 72, pp. 351-361
Open Access | Times Cited: 39
Differential evolution-based convolutional neural networks: An automatic architecture design method for intrusion detection in industrial control systems
Jiacheng Huang, Guo‐Qiang Zeng, Guanggang Geng, et al.
Computers & Security (2023) Vol. 132, pp. 103310-103310
Closed Access | Times Cited: 36
Res-TranBiLSTM: An intelligent approach for intrusion detection in the Internet of Things
Shiyu Wang, Wenxiang Xu, Yiwen Liu
Computer Networks (2023) Vol. 235, pp. 109982-109982
Open Access | Times Cited: 35
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: 30
Distributed Denial of Service Attack Detection for the Internet of Things Using Hybrid Deep Learning Model
Ahmed Ahmim, Faiz Maazouzi, Marwa Ahmim, et al.
IEEE Access (2023) Vol. 11, pp. 119862-119875
Open Access | Times Cited: 30
Identifying threats, cybercrime and digital forensic opportunities in Smart City Infrastructure via threat modeling
Yee Ching Tok, Sudipta Chattopadhyay
Forensic Science International Digital Investigation (2023) Vol. 45, pp. 301540-301540
Open Access | Times Cited: 25
NLP methods in host-based intrusion detection systems: A systematic review and future directions
Zarrin Tasnim Sworna, Zahra Mousavi, Muhammad Ali Babar
Journal of Network and Computer Applications (2023) Vol. 220, pp. 103761-103761
Open Access | Times Cited: 22
Enabling Explainable AI in Cybersecurity Solutions
Imdad Ali Shah, N. Z. Jhanjhi, Sayan Kumar Ray
Advances in computational intelligence and robotics book series (2024), pp. 255-275
Closed Access | Times Cited: 9
Deep Learning Models with Transfer Learning and Ensemble for Enhancing Cybersecurity in IoT Use Cases
Sivananda Hanumanthu, G. Anil Kumar
International Journal of Computational and Experimental Science and Engineering (2025) Vol. 11, Iss. 1
Open Access | Times Cited: 1
A bio-inspired hybrid deep learning model for network intrusion detection
MD Moizuddin, M. Victor Jose
Knowledge-Based Systems (2021) Vol. 238, pp. 107894-107894
Closed Access | Times Cited: 42
Machine Learning With Variational AutoEncoder for Imbalanced Datasets in Intrusion Detection
Ying‐Dar Lin, Ziqiang Liu, Ren‐Hung Hwang, et al.
IEEE Access (2022) Vol. 10, pp. 15247-15260
Open Access | Times Cited: 29
Effective network intrusion detection via representation learning: A Denoising AutoEncoder approach
Ivandro Ortet Lopes, Deqing Zou, Ihsan H. Abdulqadder, et al.
Computer Communications (2022) Vol. 194, pp. 55-65
Closed Access | Times Cited: 28
A data-driven network intrusion detection system using feature selection and deep learning
Lianming Zhang, Kui Liu, Xiaowei Xie, et al.
Journal of Information Security and Applications (2023) Vol. 78, pp. 103606-103606
Closed Access | Times Cited: 18
Approach for Detecting Attacks on IoT Networks Based on Ensemble Feature Selection and Deep Learning Models
Shaza Dawood Ahmed Rihan, Mohammed Anbar, Basim Ahmad Alabsi
Sensors (2023) Vol. 23, Iss. 17, pp. 7342-7342
Open Access | Times Cited: 17
Meta‐analysis and systematic review for anomaly network intrusion detection systems: Detection methods, dataset, validation methodology, and challenges
Ziadoon Kamil Maseer, Qusay Kanaan Kadhim, Baidaa Al‐Bander, et al.
IET Networks (2024) Vol. 13, Iss. 5-6, pp. 339-376
Open Access | Times Cited: 6
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