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Phishing is one of the most prevalent cyber threats, where attackers deceive users through fraudulent websites or URLs to steal sensitive information such as passwords and financial data. This paper presents a Machine Learning (ML)-based approach for phishing URL detection to enhance cybersecurity and protect users from online scams. The proposed system extracts various lexical, host-based, and content-based features from URLs, including domain length, presence of special characters, HTTPS usage, and URL age. These features are analyzed using supervised learning algorithms such as Random Forest, Decision Tree, Support Vector Machine (SVM), and Logistic Regression to classify URLs as legitimate or phishing. Data preprocessing and feature selection are performed to improve model accuracy and reduce computational complexity. Experimental results show that ensemble and hybrid ML models outperform traditional blacklist-based methods in detection accuracy and adaptability. The system can be integrated into browsers or email filters to provide real-time protection against phishing attacks.
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Copyright © 2020, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Corresponding Author: YEGIREDDI RAMESH, rameshyegireddi@gmail.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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