Phishing attacks are the simplest way to obtain sensitive information from innocent users. Aim of the phishers is to acquire critical information like username, password and bank account details. Cyber security persons are now looking for trustworthy and steady detection techniques for phishing websites detection. This paper deals with machine learning technology for the detection of phishing URLs by extracting and analyzing various features of legitimate and phishing URLs.Decision Tree, random forest, Logistic Regression and Support vector machine algorithms are used to detect phishing website.
To perform phishing website classification, the Phishing Websites Data Set from Kaggle is being used . It contains 32 columns that have 30 features and a target variable that has the label. The target variable has two classes denoting whether the website is legitimate or phishing. The dataset contains data of 11,055 legitimate (44.30 %) and phishing(55.70%).
Kaggle website :https://www.kaggle.com/datasets/eswarchandt/phishing-website-detector
Tanya Sinha Radhika Shrivastava Rohini Shrivastava