Hybrid Phishing Detection Based on Automated Feature Selection Using the Chaotic Dragonfly Algorithm

Abstract

Due to the increased frequency of phishing attacks, network security has gained the attention of researchers. In addition to this, large volumes of data are created every day, and these data include inappropriate and unrelated features that influence the accuracy of machine learning. There is therefore a need for a robust method of detecting phishing threats and improving detection accuracy. In this study, three classifiers were applied to improve the accuracy of a detection algorithm: decision tree, k-nearest neighbors (KNN), and support vector machine (SVM). Selecting the relevant features improves the detection accuracy for a target class and determines the class label with the greatest probability. The proposed work clearly describes how feature selection using the Chaotic Dragonfly Algorithm provides more accurate results than all other baseline classifiers. It also indicates the appropriate classifier to be applied when detecting phishing websites. Three publicly available datasets were used to evaluate the method. They are reliable datasets for training the model and measuring prediction accuracy.

Sectrabank Model to Mitigate Computer Fraud in Electronic Operations through Banking Applications on Android Devices

“…The Chaotic Dragonfly Algorithm was applied for the automated selection of features, along with K-nearest neighbors (KNN), SVM, and decision tree classifiers. The results showed a 20% improvement in phishing detection accuracy compared to traditional methods, demonstrating the efficacy of the proposed approach in mitigating bank fraud [46].…”