Phishing Url Detection Using Machine Learning Technique

DOI

10.2991/978-94-6463-738-0_73How to use a DOI?

Keywords

machine learning; cybersecurity; supervised learning; phishing detection; URL classification

Abstract

Phishing attacks have surged in recent years, posing a significant threat to cybersecurity. One of the main strategies used by attackers is to create fake URLs that fool users into giving away their personal information. This paper introduces a novel machine learning model designed to effectively identify phishing URLs by analyzing key characteristics and utilizing supervised learning algorithms. Our model demonstrates high accuracy in classifying URLs as either legitimate or phishing, validated through extensive testing on real-world datasets.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Preeti Tuli
AU  - Anamika Verma
AU  - Arshi Shah
AU  - Vaishnavi
PY  - 2025
DA  - 2025/06/22
TI  - Phishing Url Detection Using Machine Learning Technique
BT  - Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)
PB  - Atlantis Press
SP  - 935
EP  - 944
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-738-0_73
DO  - 10.2991/978-94-6463-738-0_73
ID  - Tuli2025
ER  -