• Vankudothu Jeevan
• Bushaboina Uday Kumar
• Nangunuri Sai Teja
• Mohan Dholvan
• K Sreelatha

Phishing attacks, which use phony websites, emails, and SMS messages to target people and organizations, are a serious threat in the digital age. The goal of this project, "PHISHNET," is to use cutting-edge machine learning techniques to create a reliable and scalable defense against phishing threats. Utilizing algorithms like Random Forest, Gradient Boost, and Naïve Bayes, the system is built to identify and stop phishing attempts instantly, offering complete defense against constantly changing online threats. Website, email, and SMS phishing detection are the three main areas covered by this project's scope. It incorporates proactive prevention measures, such as a blocking mechanism for phishing websites that have been identified, and real-time classification mechanisms to identify malicious activities. Easy interaction is ensured by a user-friendly interface, which also allows for informed decision-making by displaying phishing risk levels via safety percentages. The literature review stresses the need for updated techniques to handle small datasets and computational limitations, as well as issues with current systems, such as false positives and negatives.

Phishing, Machine learning, XG Boost, Random Forest, Naïve Bayes.

"PHISHNET: Online Spam and Phishing Detection Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 4, page no.k337-k343, April-2025, Available :http://www.jetir.org/papers/JETIR2504A41.pdf

"PHISHNET: Online Spam and Phishing Detection Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 4, page no. ppk337-k343, April-2025, Available at : http://www.jetir.org/papers/JETIR2504A41.pdf