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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:

PhishNot: A Cloud-Based Machine-Learning Approach to Phishing URL Detection
Mohammed M. Alani, Hissam Tawfik
Computer Networks (2022) Vol. 218, pp. 109407-109407
Closed Access | Times Cited: 25

Showing 25 citing articles:

Machine learning empowered computer networks
Tania Cerquitelli, Michela Meo, Marília Curado, et al.
Computer Networks (2023) Vol. 230, pp. 109807-109807
Closed Access | Times Cited: 41
An application for predicting phishing attacks: A case of implementing a support vector machine learning model
Emmanuel Song Shombot, Gilles Dusserre, Robert Bešťák, et al.
Cyber Security and Applications (2024) Vol. 2, pp. 100036-100036
Open Access | Times Cited: 8
A Multi-vocal Literature Review on challenges and critical success factors of phishing education, training and awareness
Orvila Sarker, Asangi Jayatilaka, Sherif Haggag, et al.
Journal of Systems and Software (2023) Vol. 208, pp. 111899-111899
Open Access | Times Cited: 18
Web-based phishing URL detection model using deep learning optimization techniques
Kousik Barik, Sanjay Misra, R.S. Mohan
International Journal of Data Science and Analytics (2025)
Open Access
Anti-phishing: A comprehensive perspective
Gaurav Varshney, Rahul Kumawat, Vijay Varadharajan, et al.
Expert Systems with Applications (2023) Vol. 238, pp. 122199-122199
Closed Access | Times Cited: 13
Enhancing Phishing Detection in Cloud Environments Using RNN-LSTM in a Deep Learning Framework
Oussama Senouci, Nadjib Benaouda
Journal of Telecommunications and Information Technology (2025)
Open Access
PhiUSIIL: A diverse security profile empowered phishing URL detection framework based on similarity index and incremental learning
Arvind Prasad, Shalini Chandra
Computers & Security (2023) Vol. 136, pp. 103545-103545
Closed Access | Times Cited: 12
Phishing URL detection generalisation using Unsupervised Domain Adaptation
Fariza Rashid, Ben Doyle, Soyeon Caren Han, et al.
Computer Networks (2024) Vol. 245, pp. 110398-110398
Open Access | Times Cited: 3
Detection of phishing addresses and pages with a data set balancing approach by generative adversarial network (GAN) and convolutional neural network (CNN) optimized with swarm intelligence
Somayyeh Jafari, Nasrin Aghaee‐Maybodi
Concurrency and Computation Practice and Experience (2024) Vol. 36, Iss. 11
Closed Access | Times Cited: 3
Assessment of existing cyber-attack detection models for web-based systems
Odiaga Gloria Awuor
Global Journal of Engineering and Technology Advances (2023) Vol. 15, Iss. 1, pp. 070-089
Open Access | Times Cited: 4
Understanding the Influence of AST-JS for Improving Malicious Webpage Detection
Muhammad Fakhrur Rozi, Seiichi Ozawa, Tao Ban, et al.
Applied Sciences (2022) Vol. 12, Iss. 24, pp. 12916-12916
Open Access | Times Cited: 4
Soft computing in computer network security protection system with machine learning based on level protection in the cloud environment
Merin Thomas, Mohan Vishal Gupta, V. Gokul Rajan, et al.
Soft Computing (2023)
Closed Access | Times Cited: 2
Enhancing Mobile Cybersecurity: Smishing Detection Using Ensemble Learning and Smote
Hongsheng Xu, Akeel Qadir, Saima Sadiq
(2024)
Closed Access
Machine Learning-Based Phishing Website Detection: A Comparative Analysis and Web Application Development
Jia Xin Yau, Kai Lin Chia
Lecture notes in networks and systems (2024), pp. 175-185
Closed Access
Enhanced Phishing Website Detection Using Dual-Layer CNN and GRU with Attention Mechanism and Lexical NLP Features
Santosh Kumar Birthriya, Priyanka Ahlawat, Ankit Kumar Jain
SN Computer Science (2024) Vol. 5, Iss. 7
Closed Access
PhiSN: Phishing URL Detection Using Segmentation and NLP Features
Eint Sandi Aung, Hayato Yamana
Journal of Information Processing (2024) Vol. 32, pp. 973-989
Open Access
Navigating Data Imbalances in Cybersecurity: Identifying Malicious URLs with Multiple Labels and Extreme Data Imbalances with LGNet
Anran Zhu, Yubo Huang, Xin-Ji Lai
Lecture notes in computer science (2024), pp. 87-102
Closed Access
LLMs are one-shot URL classifiers and explainers
Fariza Rashid, Nishavi Ranaweera, Ben Doyle, et al.
Computer Networks (2024), pp. 111004-111004
Open Access
A Hybrid Algorithm for Detection of Cloud-Based Email Phishing Attack
Saahira Banu Ahamed, Anne Anoop, Rejna Azeez Nazeema, et al.
Communications in computer and information science (2024), pp. 177-187
Closed Access
RPCP-PURI: A robust and precise computational predictor for Phishing Uniform Resource Identification
Tayyaba Asif, Faiza Mehmood, Syed Ahmed Mazhar Gillani, et al.
Journal of Information Security and Applications (2024) Vol. 89, pp. 103953-103953
Closed Access
A Machine Learning Model for Predicting Phishing Websites
Grace Odette Boussi, Himanshu Gupta, Syed Akhter Hossain
Research Square (Research Square) (2023)
Open Access | Times Cited: 1
PhishHunter: Detecting camouflaged IDN-based phishing attacks via Siamese neural network
Maoli Wang, Xiaodong Zang, Jianbo Cao, et al.
Computers & Security (2023) Vol. 138, pp. 103668-103668
Closed Access | Times Cited: 1
Soft computing in computer network security protection system with machine learning based on level protection in the cloud environment
Merin Thomas, Mohan Vishal Gupta, Gokul Rajan, et al.
Research Square (Research Square) (2023)
Open Access
Personalized Guidelines for Design, Implementation and Evaluation of Anti-Phishing Interventions
Orvila Sarker, Sherif Haggag, Asangi Jayatilaka, et al.
(2023), pp. 1-12
Open Access
Detection of Healthcare Phishing Websites Using Machine Learning
Deepak Rao Khadatkar, Yugmani Sahu, Nivesh Pole, et al.
(2023), pp. 1-6
Closed Access
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