Whale Optimization and AutoML for Precise Phishing Detection

Abstract

Online fraud and social engineering tactics frequently use phishing websites as platforms. Phishers often modify the source code of the web pages they exploit in their attacks to create the illusion that alterations were made to authentic websites. A solitary response is insufficient to mitigate phishing due to the many methods employed in its execution. This study examines machine learning algorithms and evaluates their efficacy when trained on datasets including attributes that differentiate secure websites from phishing sites. Automated algorithms facilitate real-time fraud protection by swiftly detecting suspicious URLs, domain names, and website content. This study aims to identify the optimal method for detecting a prevalent category of cyberattacks. This would enhance the security and privacy of all internet users by facilitating the identification and blocking of malicious websites. Nonetheless, there is an urgent desire for automated models that provide rapid and precise detection. This research introduces a regression-based assessment method for phishing detection to address this demand. Our approach employs a whale optimization algorithm for feature selection. An AutoML framework subsequently utilizes the selected feature subsets as input. The model showed good accuracy in its predictions with very small errors on the test data, shown by an RMSE of 0.1079, an MSE of 0.0116, and an R2 value of 0.9534. These results demonstrate the reliability of our feature selection and modeling methods.