Classification of Phishing Websites using Machine Learning Models
Santhosh Raminedi, Trilok Nath Pandey, Venkat Amith Woonna, Sletzer Concy Mascarenhas, Arjun Bharani · 2023
Phishing attacks have become increasingly and it is a serious threat to security of the user information. It targets unsuspecting individuals through deceptive websites that appear to be legitimate. These attacks can compromise sensitive information and also have serious consequences. Machine learning has a potential solution to address the growing threat of phishing. By training algorithms to recognize the tell-tale signs of phishing websites, it may be possible to detect and prevent these attacks before they can cause harm. In our paper, we explored the features of detecting the phishing url with the use of ML algorithms such as SVM, Logistic Regression, Naive Bayes classifier, and also ensembling algorithm such as Random Forest Classifier. We examine the features that are commonly used in these algorithms and compare their effectiveness in accurately classifying websites as either Phishing or Legitimate