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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:

Deep Reinforcement Learning for Cyber Security
Thanh Thi Nguyen, Vijay Janapa Reddi
IEEE Transactions on Neural Networks and Learning Systems (2021) Vol. 34, Iss. 8, pp. 3779-3795
Open Access | Times Cited: 315

Showing 1-25 of 315 citing articles:

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security
Mohammed Ali Al-Garadi, Amr Mohamed, Abdulla Al‐Ali, et al.
IEEE Communications Surveys & Tutorials (2020) Vol. 22, Iss. 3, pp. 1646-1685
Open Access | Times Cited: 922
Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges
Michele Polese, Leonardo Bonati, Salvatore D’Oro, et al.
IEEE Communications Surveys & Tutorials (2023) Vol. 25, Iss. 2, pp. 1376-1411
Open Access | Times Cited: 414
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: 344
Application of deep reinforcement learning to intrusion detection for supervised problems
Manuel López-Martín, Belén Carro, Antonio Sánchez-Esguevillas
Expert Systems with Applications (2019) Vol. 141, pp. 112963-112963
Open Access | Times Cited: 271
A Review of Deep Reinforcement Learning for Smart Building Energy Management
Liang Yu, Shuqi Qin, Meng Zhang, et al.
IEEE Internet of Things Journal (2021) Vol. 8, Iss. 15, pp. 12046-12063
Open Access | Times Cited: 248
Artificial intelligence, cyber-threats and Industry 4.0: challenges and opportunities
Adrien Bécue, Isabel Praça, João Gama
Artificial Intelligence Review (2021) Vol. 54, Iss. 5, pp. 3849-3886
Closed Access | Times Cited: 226
The AI-Based Cyber Threat Landscape
Nektaria Kaloudi, Jingyue Li
ACM Computing Surveys (2020) Vol. 53, Iss. 1, pp. 1-34
Closed Access | Times Cited: 191
Deep Learning Algorithms for Cybersecurity Applications: A Technological and Status Review
Priyanka Dixit, Sanjay Silakari
Computer Science Review (2020) Vol. 39, pp. 100317-100317
Closed Access | Times Cited: 178
The Role of Machine Learning in Cybersecurity
Giovanni Apruzzese, Pavel Laskov, Edgardo Montes de, et al.
Digital Threats Research and Practice (2022) Vol. 4, Iss. 1, pp. 1-38
Open Access | Times Cited: 121
Deep Q-Learning Based Reinforcement Learning Approach for Network Intrusion Detection
Hooman Alavizadeh, Hootan Alavizadeh, Julian Jang‐Jaccard
Computers (2022) Vol. 11, Iss. 3, pp. 41-41
Open Access | Times Cited: 118
AE-MLP: A Hybrid Deep Learning Approach for DDoS Detection and Classification
Yuanyuan Wei, Julian Jang‐Jaccard, Fariza Sabrina, et al.
IEEE Access (2021) Vol. 9, pp. 146810-146821
Open Access | Times Cited: 116
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: 91
A novel method for improving the robustness of deep learning-based malware detectors against adversarial attacks
Kamran Shaukat, Suhuai Luo, Vijay Varadharajan
Engineering Applications of Artificial Intelligence (2022) Vol. 116, pp. 105461-105461
Closed Access | Times Cited: 90
Artificial Intelligence-Based Cyber Security in the Context of Industry 4.0—A Survey
Antonio João Gonçalves de Azambuja, Christian Plesker, Klaus Schützer, et al.
Electronics (2023) Vol. 12, Iss. 8, pp. 1920-1920
Open Access | Times Cited: 84
Explainable Intrusion Detection for Cyber Defences in the Internet of Things: Opportunities and Solutions
Nour Moustafa, Nickolaos Koroniotis, Marwa Keshk, et al.
IEEE Communications Surveys & Tutorials (2023) Vol. 25, Iss. 3, pp. 1775-1807
Closed Access | Times Cited: 76
A Systematic Study on Reinforcement Learning Based Applications
Keerthana Sivamayilvelan, R Elakkiya, Belqasem Aljafari, et al.
Energies (2023) Vol. 16, Iss. 3, pp. 1512-1512
Open Access | Times Cited: 63
Survey on Intrusion Detection Systems Based on Machine Learning Techniques for the Protection of Critical Infrastructure
Andrea Pinto, Luis-Carlos Herrera, Yezid Donoso, et al.
Sensors (2023) Vol. 23, Iss. 5, pp. 2415-2415
Open Access | Times Cited: 62
A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions
Merve Ozkan-Okay, Erdal Akin, Ömer Aslan, et al.
IEEE Access (2024) Vol. 12, pp. 12229-12256
Open Access | Times Cited: 43
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: 41
Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning Solutions
Easa Alalwany, Imad Mahgoub
Sensors (2024) Vol. 24, Iss. 2, pp. 368-368
Open Access | Times Cited: 30
A survey on graph neural networks for intrusion detection systems: Methods, trends and challenges
Meihui Zhong, Ming‐Wei Lin, Chao Zhang, et al.
Computers & Security (2024) Vol. 141, pp. 103821-103821
Closed Access | Times Cited: 29
An evolutionary KNN model for DDoS assault detection using genetic algorithm based optimization
Fizza Rizvi, Ravi Sharma, Nonita Sharma, et al.
Multimedia Tools and Applications (2024) Vol. 83, Iss. 35, pp. 83005-83028
Closed Access | Times Cited: 15
Reinforcement Learning for IoT Security: A Comprehensive Survey
Aashma Uprety, Danda B. Rawat
IEEE Internet of Things Journal (2020) Vol. 8, Iss. 11, pp. 8693-8706
Open Access | Times Cited: 133
Utilising Deep Learning Techniques for Effective Zero-Day Attack Detection
Hanan Hindy, Robert Atkinson, Christos Tachtatzis, et al.
Electronics (2020) Vol. 9, Iss. 10, pp. 1684-1684
Open Access | Times Cited: 130
Deep Reinforcement Learning for Traffic Signal Control: A Review
Faizan Rasheed, Kok‐Lim Alvin Yau, Rafidah Md Noor, et al.
IEEE Access (2020) Vol. 8, pp. 208016-208044
Open Access | Times Cited: 91
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