A Novel Approach for Phishing URLs Detection

Seeking sensitive user data in the form of online banking user-id and passwords or credit card information, which may then be used by phishers for their own personal gain is the primary objective of the phishing. With the increase in the online trading activities, there has been a phenomenal increase in the phishing scams which have now started achieving monstrous proportions. This paper gives strategies for distinguishing phishing sites by dissecting different components of phishing URLs by Machine learning systems. It talks about the systems utilized for identification of phishing sites in view of lexical features. We consider different data mining approaches for assessment of the features to show signs of improvement comprehension of the structure of URLs that spread phishing. We use KNN, Regression and SVM classifiers.