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Phishing Website URL’s Detection Using NLP and Machine Learning Techniques

Dinesh Kalla1,*, Sivaraju Kuraku2

1 Department of Computer Science, Colorado Technical University, Colorado Springs, CO, 80907, USA
2 Department of Computer Science, University of the Cumberlands, Williamsburg, KY, 40769, USA

* Corresponding Author: Dinesh Kalla. Email: email

Journal on Artificial Intelligence 2023, 5, 145-162.


Phishing websites present a severe cybersecurity risk since they can lead to financial losses, data breaches, and user privacy violations. This study uses machine learning approaches to solve the problem of phishing website detection. Using artificial intelligence, the project aims to provide efficient techniques for locating and thwarting these dangerous websites. The study goals were attained by performing a thorough literature analysis to investigate several models and methods often used in phishing website identification. Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forests, Support Vector Classifiers, Linear Support Vector Classifiers, and Naive Bayes were all used in the inquiry. This research covers the benefits and drawbacks of several Machine Learning approaches, illuminating how well-suited each is to overcome the difficulties in locating and countering phishing website predictions. The insights gained from this literature review guide the selection and implementation of appropriate models and methods in future research and real-world applications related to phishing detections. The study evaluates and compares accuracy, precision and recalls of several machine learning models in detecting phishing website URL’s detection.


Cite This Article

APA Style
Kalla, D., Kuraku, S. (2023). Phishing website url’s detection using NLP and machine learning techniques. Journal on Artificial Intelligence, 5(1), 145-162.
Vancouver Style
Kalla D, Kuraku S. Phishing website url’s detection using NLP and machine learning techniques. J Artif Intell . 2023;5(1):145-162
IEEE Style
D. Kalla and S. Kuraku, "Phishing Website URL’s Detection Using NLP and Machine Learning Techniques," J. Artif. Intell. , vol. 5, no. 1, pp. 145-162. 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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