Published Paper Details:

Currently, numerous types of cybercrime are organized through the internet. Hence, this study mainly focuses on phishing attacks. Although phishing was first used in 1996, it hasbecome the most severe and dangerous cybercrime on the internet. Phishing utilizes email distortion as its underlying mechanism for tricky correspondences, followed by mock sites, to obtain the required data from people in question. Different studies have presentedtheir work on the precaution, identification, and knowledge of phishing attacks; however, there is currently no complete and proper solution for frustrating them. The proposed project is based on the phishing URL-based dataset extracted from the famous dataset repository, Which contains phishing and legal URL features collected from 11000+ website datasets in vector form. After preprocessing, several machine learning algorithms are implemented and designed to block phishing URLs and provide security to the user. This project uses machine learning models like Decision Tree (DT), Linear Regression (LR), Random Forest (RF), Naive Bayes (NB), Gradiant Boosting Classifier (GBM), K-Neighbors Classifier (KNN), Support Vector Classifier. Does. (SVC), and the proposed hybrid LSD model, which is a combination of logistic regression, support vector machine and decision tree (LR + SVC + DT) with soft and hard voting to protect against phishing attacks with high accuracy and efficiency.