Detection of Phishing Link Using Different Machine Learning Techniques

The growing threat of phishing attacks on the Internet has raised concerns about the security of personal and organizational information. The use of machine learning techniques has emerged as a potential remedy for detecting and preventing phishing attacks. In this study, we compare and contrast different types of analysis of machine learning algorithms for predicting phishing websites. We measured how well different models did by looking at their accuracy, precision, recall, and F1-score metrics. In terms of classification accuracy, we discovered that ensemble learning techniques like random forest and decision tree outperformed other models. We also analyzed the feature importance of each algorithm to identify the most discriminative features for predicting phishing websites. Our discoveries offer significant perspectives on the topic of the effectiveness of machine learning approaches in detecting phishing attacks, which can help to enhance the security of online transactions and prevent data breaches.