  Now-a-days, cyberattacks are increasing at an unprecedented rate. Phishing is
a social engineering attack which has a massive global impact, destroying the
financial and economic value of corporations, government sectors and
individuals. In phishing, attackers steal users personal information such as
username, passwords, debit card information and so on. In order to detect
zero-hour attacks and protect end-users from these attacks, various
anti-phishing techniques are developed, but the end-users have to visit the
websites to know whether they are safe or not, which may lead to infecting
their system. In this paper, we propose a method where end-users can detect the
genuineness of the sites without visiting them. The proposed method collects
legitimate and phishing URLs and extract features from them. The extracted
features are given as input to six different classifiers for training and
constructing the model. The classifiers used are Naive-Bayes, Logistic
Regression, Random Forest,CatBoost, XGBoost and Multilayer perceptron. The
method is tested by developing into an extension so that the end-users can use
it when browsing. In the browser extension when the user takes the cursor over
any link, a pop-up appears showing the nature of the website i.e., safe site or
deceptive site and then a confirm box shows up asking the user whether they
want to visit or not. The performance of the approach is tested using a dataset
consisting of 2000 phishing and legitimate website URLs and the method is able
to detect the sites correctly in very little time. Random-Forest is chosen for
constructing the model as it gives the highest accuracy of 95%.
