• Manish Deore
• Prof.H. P. Bhabad

Phishing attacks continue to be a prevalent and evolving cybersecurity threat, exploiting human vulnerabilities to steal sensitive information through deceptive websites that mimic legitimate entities. Traditional detection methods, such as blacklists, often struggle to keep pace with the rapid emergence of new phishing sites, highlighting the need for more dynamic and adaptive solutions. In this article focus on investigates machine learning-based approaches to effectively identify and mitigate phishing attempts. By analyzing various features extracted from Uniform Resource Locators (URLs) and potentially website content, machine learning models can learn to differentiate between legitimate and fraudulent websites. The methodology involves data collection, comprehensive feature extraction, and the application of diverse machine learning algorithms to build robust classification models. Our work evaluates the performance of multiple machine learning classifiers, such as Random Forest, Decision Trees, and Support Vector Machines, on datasets containing both legitimate and phishing website URLs. The objective is to identify optimal models and feature selection techniques that yield high accuracy, precision, and recall in detecting sophisticated phishing attacks. Experimental results consistently demonstrate the efficacy of machine learning in achieving significant detection rates, contributing to a more secure online environment by enabling timely identification of malicious URLs without reliance on constantly updated blacklists. This research provides valuable insights into developing effective anti-phishing systems, offering a proactive defense against evolving cyber threats.

Cybersecurity, Phishing, Machine Learning, URL Analysis, Website Classification, Anti-phishing, Feature Extraction.

"URL-Based Phishing Detection Leveraging Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 6, page no.a856-a867, June-2025, Available :http://www.jetir.org/papers/JETIR2506090.pdf

"URL-Based Phishing Detection Leveraging Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 6, page no. ppa856-a867, June-2025, Available at : http://www.jetir.org/papers/JETIR2506090.pdf