Paper Contents
Abstract
This Project presents a novel approach for Detecting Phishing Websites and Spam Content using Machine Learning algorithms. Specifically, we focus on using the K-Neighbors Classifier algorithm to accurately identify and classify suspicious websites and content. Our proposed approach involves training the model on a large dataset of known Phishing Website and Spam Content, and then using the model to classify new websites and content based on their features and characteristics. We demonstrate the effectiveness of our approach through extensive experimentation and evaluation on a Real-World Dataset. Our results show that our approach is highly effective in detecting and classifying phishing websites and spam content, achieving high accuracy and low false positive rates. Overall, our projects present a promising approach to combatting web phishing attacks using machine learning techniques.
Copyright
Copyright © 2023 Prasad N, Anthoni Thomas R, Karthikeyan D, Pon Pandian P, Sekar S. This is an open access article distributed under the Creative Commons Attribution License.
