PHISHING WEB SITES FEATURES CLASSIFICATION BASED ON MACHINE LEARNING

computer engineering

Phishing is one of the most common and most dangerous attacks among cybercrimes. The main aim of these attack is to hack the user information by accessing the credentials that is used by individuals and any of the organizations. Phishing web sites contains various hints among their contents and web browser-based information. The victim’s confidential data is expected by the phishing sites by deriving them to surf a phishing web sites that resembles to legitimate websites, which is one of the criminal attacks prevailing in the internet. Phishing websites is similar to cyber threat that is targeting to get all the credential-based information accessed from the credit cards and social security numbers. The purpose of this project is to perform Extreme Learning Machine (ELM) based classification. There are different types of features based on web pages. Hence, to prevent phishing attacks we must use a specific web page feature. Here, a model based on Machine Learning techniques like Naïve Bayes is used to detect phishing web pages.

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