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Cited by 13
Comparison of Classification Algorithms for Detection of Phishing Websites

Machine Learning-Based Phishing Detection Using URL Features: A Comprehensive Review
Asif Uz Zaman Asif, Hossein Shirazi, Indrakshi Ray
https://doi.org/10.1007/978-3-031-44274-2_36
Book:  Lecture Notes in Computer Science (Stabilization, Safety, and Security of Distributed Systems) Volume 14310 (2023), p. 481
Composition of Ensembles of Recurrent Neural Networks for Phishing Websites Detection
Paulius Vaitkevicius, Virginijus Marcinkevicius
https://doi.org/10.1007/978-3-030-57672-1_22
Book:  Communications in Computer and Information Science (Databases and Information Systems) Volume 1243 (2020), p. 297
An Empirical Feature Selection Approach for Phishing Websites Prediction with Machine Learning
Pankaj Bhowmik, Md. Sohrawordi, U. A. Md. Ehsan Ali, Pulak Chandra Bhowmik
https://doi.org/10.1007/978-3-031-17181-9_14
Book:  Communications in Computer and Information Science (Bangabandhu and Digital Bangladesh) Volume 1550 (2022), p. 173
2022 International Conference on Informatics Electrical and Electronics (ICIEE)
Bianca Chelsea Putri Nugroho, Josephine Florencia Chan, Valencia Vananda, Henry Lucky, Derwin Suhartono
https://doi.org/10.1109/ICIEE55596.2022.10009939
Conference:  (2022), p. 1
A Machine Learning Approach for Phishing Websites Prediction with Novel Feature Selection Framework
Pankaj Bhowmik, Pulak Chandra Bhowmik
https://doi.org/10.1007/978-981-19-2445-3_24
Book:  Lecture Notes in Networks and Systems (Proceedings of International Conference on Fourth Industrial Revolution and Beyond 2021) Volume 437 (2022), p. 357
2022 5th International Conference on Information and Communications Technology (ICOIACT)
Anthony Chandra, Gregorius, M. S. John Immanuel, Alexander Agung Santoso Gunawan, Anderies
https://doi.org/10.1109/ICOIACT55506.2022.9972107
Conference:  (2022), p. 24
Intelligent feature selection model based on particle swarm optimization to detect phishing websites
Theyab R. Alsenani, Safial Islam Ayon, Sayeda Mayesha Yousuf, Fahad Bin Kamal Anik, Mohammad Ehsan Shahmi Chowdhury
https://doi.org/10.1007/s11042-023-15399-6
Journal:  Multimedia Tools and Applications Volume 82, Issue 29 (2023), p. 44943
NETWORK INTRUSION DETECTION USING HYBRID MACHINE LEARNING METHODS
Karina Čiurlienė, Denisas Stankevičius
https://doi.org/10.3846/mla.2023.19385
Journal:  Mokslas - Lietuvos ateitis Volume 15 (2023), p. 1
Overview of Machine Learning Process Modelling
Boštjan Brumen, Aleš Černezel, Leon Bošnjak
https://doi.org/10.3390/e23091123
Journal:  Entropy Volume 23, Issue 9 (2021), p. 1123
Performance Evaluation of Phishing Classification Techniques on Various Data Sources and Schemes
Rahmad Abdillah, Zarina Shukur, Masnizah Mohd, T. S. Mohd Zamri Murah, Insu Oh, Kangbin Yim
https://doi.org/10.1109/ACCESS.2022.3225971
Journal:  IEEE Access Volume 11 (2023), p. 38721
Phishing Classification Techniques: A Systematic Literature Review
Rahmad Abdillah, Zarina Shukur, Masnizah Mohd, Ts. Mohd Zamri Murah
https://doi.org/10.1109/ACCESS.2022.3166474
Journal:  IEEE Access Volume 10 (2022), p. 41574
Stop-Phish: an intelligent phishing detection method using feature selection ensemble
A. V. Ramana, K. Lakshmana Rao, Routhu Srinivasa Rao
https://doi.org/10.1007/s13278-021-00829-w
Journal:  Social Network Analysis and Mining Volume 11, Issue 1 (2021)
URL filtering using machine learning algorithms
Asia Othman Aljahdalic, Shoroq Banafee, Thana Aljohani
https://doi.org/10.1080/19393555.2023.2193350
Journal:  Information Security Journal: A Global Perspective Volume 33, Issue 3 (2024), p. 193


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