An Ideal Approach for Detection of Phishing Attacks using Naïve Bayes Classifier

Phishing attack is an aberrant trick to peculate user’s private information by duping them to assail via a spurious website planned to mimic and resembles as an authentic website. The user’s confidential information such as username, password, and PIN number will be grabbed by the attacker and creates a fraudulent transactions. The information holder’s credentials as well as money will be seized. The phishing and legitimate website will have high intelligible resemblances by which the attacker will seize the credentials of the user. Inorder to detect the phishing attacks there exists various techniques such as blacklisting, whitelisting, heuristics and machine learning. Nowadays machine learning is used and found to be more effective. The proposed system extracts the source code features, URL features and image features from the phishing website. The features that are extracted are given to the ant colony optimization algorithm to acquire the reduced features. The reduced features are again given to the Naïve Bayes classifier inorder to classify the webpage as genuine or phished.