Detecting Phishing Websites Using Neural Network and Bayes Classifier

Abstract

Phishing is a social engineering attack that is based on a cyberattack and it focuses on naïve online users by spoofing them to provide their sensitive credentials such as password, username, security number, or debit card number, etc. Phishing can be performed by masking a webpage as a legitimate page to pull the personal credentials of the user. Nonetheless, there are a lot of methodologies that have been introduced as a solution for detecting the phishing websites such as the whitelist approach or blacklist approach, visual similarity-based approach, and meta-heuristic approach however still the online users are getting scammed into revealing sensitive credentials in phishing websites. In this research paper, a novel hybrid methodology PB-cup learner was proposed, which is based on integration dimensional and neural learning that is pulled from the source code, uniform resource locator, and representative state transfer API to overcome the drawbacks of the existing phishing techniques. This model gives the accuracy analysis of the Naïve Bayes Classifier, Genetic Algorithm, Multi-Layer Perceptron, Multiple Linear Regression, and PB-CUP neural learner and out of which, the Multi-Layer Perceptron algorithm has been performed the best with an accuracy of 99.17%. The experiments were iteratively analyzed with different orthogonal algorithms for finding the best classifier accuracy for phishing website detection.