Naïve Bayes Classifier with Genetic Algorithm for Phishing Website Detection

Online phishing is one of the most serious cyber-attacks, with the goal of fraudulently obtaining sensitive information from internet users. Site and Common Resource Locator (URL) phishing occurs when attackers use phishing websites that appear identical to legitimate websites to trick end users into disclosing personal information. Passwords and credit card numbers are examples of sensitive factors. Several techniques have been proposed to address this issue. Machine learning algorithms have proven to be extremely effective in this field. This paper describes a method for detecting phishing websites that is based on the Naive Bayes classifier (GA) and the genetic algorithm (GA). These algorithms are combined to form a wrapped feature selection algorithm, which searches the feature space for a useful and relevant feature. This will then aid in determining whether the sites are genuine or fraudulent. To discover the optimal subset, GA was used to generate feature subsets and the NB classifier was used to evaluate the classification accuracy of feature subsets generated with GA. The proposed method has been tested on three phishing site datasets. On all data sets, experimental results show that NB combined with GA can achieve accuracy of up to 91%. Furthermore, less than 40% of the features in all datasets considered were identified as informative and important, while other features were excluded from the feature space.