Volume 18, No. 6, 2021

An Improved Classification Model For Identifying The Phishing Attacks


Vinod Sapkal , Dr. Ninad More

Abstract

An attack known as phishing is a malicious scheme that is used to steal private information from users by tricking them into attacking via a fake website that has been designed to imitate and look very similar to an actual website. The perpetrator of the attack will steal the user's private information, including their username, password, and personal identification number (PIN), and then use that information to make fraudulent transactions. The credentials of the information holder, as well as any money they may have, will be seized. The phishing website and the legitimate website will have a high degree of understandable similarity, which will allow an attacker to steal the user's credentials from the legitimate website. There are a variety of methods available, including blacklisting, whitelisting, heuristics, and machine learning, which can be utilised in order to detect phishing attempts. Learning machines are used these days, and it has been proven that they are more effective. The phishing website's source code features, as well as its URL features and picture features, are all extracted by the proposed approach. In order to obtain the reduced features, the characteristics that have been extracted are input into the algorithm for ant colony optimization. The decreased features are then provided to the Naive Bayes classifier once more in order to determine whether the website in question is authentic or phished.


Pages: 7056-7062

Keywords: Phishing, Ant colony Optimization, Na´ve Bayes Classifier, Feature Extraction.

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