PHISHING DETECTION USING MACHINE LEARNING

Abstract

Phishing is the way toward pulling individuals to dangerous attacks by manipulating them visit false sites and enter secret information like credit-card numbers, usernames and passwords. Those fake sites look very similar to the original ones making it very difficult for individuals to notice the difference. Phishing is like a game where artists try to get personal data from unaware clients. These messages often seem to be shockingly genuine, the Web pages too, wherein the approach towards clients to enter their personal data seem to be genuine. Phishing relates to fishing, but rather than catching fish, phishers try to illegally get data from the users. That is why it is very important to find a way that automatically detects such dangerous sites. The first step towards solving such a problem is collection of phishing images in order to extract different features able to make the right classification between dangerous and non-dangerous websites. are used Two algorithms for feature extraction from images dataset were used and they are called SIFT and SURF. As the data is extracted it is then structured using OCR in order to use it for classification using Machine Learning algorithms. Logistic Regression, SVM, Random Forest classifier algorithms were used on the data consisting of the feature extracted from the images. Accuracy metrics were used to quantify the classification performance of the algorithms. Random Forest achieved relatively good performance of 84.15% correct predictions and F1 score max value 89.93 %, and this was the best performance among the classifiers.