SVM Block Based Neural Learning Technique for Identification of Fraudulent Web Pages

Abstract

Due to rapid growth in internet there are more malicious webpages are grown, So it is important to detect and control such malicious webpages to avoid problems in Social Networks, Net-Banking, Business and so on. The dangers of these websites have created a demand for safeguards that protect end-users from visiting them. In an existing technique they detect webpage using uniform resource locator (URL) by using random forest and decision trees to detect and categorize malicious websites automatically. The cookies, HTTP Headers, Lock Icon, Privacy Policy, SSL Certification, Badge Verification, URL Query Strings are utilized for malicious webpages classification using (Support Vector Machine) SVM classifier.