Phishing Website URL Detection using Machine Learning - Dipayan Sinha, Dipayan Sinha, Prof. Anitha Sandeep (International Journal of Advanced Science and Technology, 2020)
Phishing is a breach of information security through which attackers can gain access to sensitive usercredentials by using counterfeit websites closely resembling legitimate websites. Phishing attacks arethe most common form of cyber-attacks achieved by cleverly disguising website URLs to trickcredulous users. With increasing number of new phishing attacks, the use of machine learningalgorithms to classify websites as phishing and legitimate has been proposed in this paper. The datasetfor this study comprises of 96,018 URLs comprising of both phishing and legitimate websites. TheURLs have been parsed using Pandas and Urllib in order to extract useful features that could help inphishing detection. Different ML algorithms such as Random Forest, Decision Tree, Adaboost, FuzzyPattern Trees etc. have been implemented on the data and a comparison is drawn between them.Random Forest Algorithm proved to be the most accurate algorithm with 95.82% accuracy.