• Pakki Sai Sireesha
• Konna Padmini
• Boyina Balu
• Bommalata suryateja
• U.D.Prasan

The proliferation of cybersecurity threats, particularly in the form of malicious websites, has become a pressing concern with the widespread use Software Devices for Various purposes. Malicious websites host content ranging from spam and malware to inappropriate ads and spoofing, posing a significant risk to users who may fall victim to scams, resulting in financial loss, private information disclosure, malware installations, and other detrimental consequences. The financial toll of such incidents amounts to billions of rupees each year. Traditional detection methods, primarily reliant on blacklists, prove insufficient in addressing the evolving nature of cyber threats, particularly in identifying newly generated malicious URLs. This project seeks to address this gap by leveraging various machine learning algorithms to proactively detect and classify malicious URLs. Treating the problem as a multi-class classification challenge, raw URLs are categorized into different types, including benign or safe URLs, phishing URLs, malware URLs, and defacement URLs using various machine learning algorithms. The primary objective is to reduce cyberattacks by preventing users from clicking on malicious URLs, thus averting potential financial and informational losses.

Malicious, URL’s, Random Forest, Machine Learning, Classification, Phishing.

" Malicious URL's Detection Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.f382-f389, March-2024, Available :http://www.jetir.org/papers/JETIR2403547.pdf

" Malicious URL's Detection Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppf382-f389, March-2024, Available at : http://www.jetir.org/papers/JETIR2403547.pdf