Detection of Phishing websites using Machine Learning

DOI

10.2991/978-94-6463-858-5_59How to use a DOI?

Keywords

Phishing websites; Gradient Boosting; Machine Learning; Cyber Security; Website classification

Abstract

This project addresses the pressing challenge of the current world which is detecting the phishing websites and avoiding those websites to keep us safe. Phishing is a prevalent threat that endangers digital security. To deal with the evolving nature of these attacks and the limitations of traditional rule-based systems, we’re turning to machine learning (ML). We’re using a variety of features from the detailed dataset that covers common phishing indicators. Our approach hones in on training and fine-tuning ML algorithms to stay sharp and proactive. By carefully evaluating and optimizing our models, we’re showing how effective our approach is at quickly spotting potential phishing websites with high accuracy rates. Our work significantly contributes to the field of cybersecurity by presenting a proactive defence mechanism that is capable of adapting to evolving phishing tactics. By emphasizingthe utilization of Machine Learning techniques, we achieve a robust system that aids in preventing fraudulent activities. The findings of these methods show the effectiveness of Machine Learning approaches in strengthening online security and enabling users and organizations to mitigate risks associated with phishing attacks. This research not only showcases the viability of ML in tackling phishing but also underscores the importance of advancements in cybersecurity measures. The outcomes highlight the potential for future enhancements and innovations in threat detection, ultimately creating a safer digital environment for users worldwide. In summary, our project emphasizes the pivotal role of machine learning in fortifying defences against phishing attacks, paving the way for enhanced cybersecurity measures.

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  - D. Sowjanya
AU  - Sravani Kuppili
AU  - Naveen Sai Sarasa
AU  - Eepsitha Singumahanati
AU  - Sai Avinash Tamminaina
PY  - 2025
DA  - 2025/11/04
TI  - Detection of Phishing websites using Machine Learning
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 680
EP  - 695
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_59
DO  - 10.2991/978-94-6463-858-5_59
ID  - Sowjanya2025
ER  -