WEBSHIELD: Phishing Website Detection using Machine Learning

WEBSHIELD: Phishing Website Detection using Machine Learning

Soumya A1, Chandrashekar KM2, Prajwal3, Vijay Kumar S Biradar4, D Prashanth Reddy5

Department of Information Science and Engineering, Rao Bahadur Y Mahabaleshwarappa Engineering College

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Abstract - Phishing is an online hoax whereby an intruder sends fraudulent messages which appear to be from a credible source. There will be included in the letter a URL or document, clicking which will capture private details or install a virus on a computer. Phishing traditionally used to take place by sending mass-scale spam campaigns targeting large populations randomly. The objective was to make as many individuals click on a link or open an infected file as possible. There are different methods to identify this kind of attack. One of the methods is machine learning. The URLs received by the user will be input to the machine learning model then the algorithm will execute the input and show the output whether it is phishing or not.There are different ML algorithms such as SVM,Neural Networks, Random Forest, Decision Tree, XG boost etc. which can be employed to classify these URLs. By comparing and extracting various features between legitimate and phishing URLs, the proposed method employs gradient boosting classifier to detect phishing URLs. The findings of the studies prove that the proposed approach effectively identifies legitimate websites from fake ones in real time

Key Words: URL classification, Machine Learning, Spam campaigns, Gradient Boosting Classifier, SVM, Neural Networks, Real-time detection, Feature extraction