Phishing Website Classification and Detection Using Machine Learning

Abstract

The phishing website has evolved as a major cybersecurity threat in recent times. The phishing websites host spam, malware, ransomware, drive-by exploits, etc. A phishing website many a time look-alike a very popular website and lure an unsuspecting user to fall victim to the trap. The victim of the scams incurs a monetary loss, loss of private information and loss of reputation. Hence, it is imperative to find a solution that could mitigate such security threats in a timely manner. Traditionally, the detection of phishing websites is done using blacklists. There are many popular websites which host a list of blacklisted websites, e. g. PhisTank. The blacklisting technique lack in two aspects, blacklists might not be exhaustive and do not detect a newly generated phishing website. In recent times machine learning techniques have been used in the classification and detection of phishing websites. In, this paper we have compared different machine learning techniques for the phishing URL classification task and achieved the highest accuracy of 98% for Naïve Bayes Classifier with a precision=1, recall = .95 and F1-Score= .97.