Phishing Website Detection using Multilayer Perceptron

Abstract

Phishing attacks pose a significant threat in the cyber world, exploiting unsuspecting users through deceptive emails that lead them to malicious websites. To combat this challenge, various deep learning based anti-phishing techniques have been developed. However, these models often suffer from high false positive rates or lower accuracy. In this study, we evaluate the performance of two neural networks, the Autoencoder and Multilayer Perceptron (MLP), using a publicly available dataset to build an efficient phishing detection model. Feature selection was performed through correlation analysis, and the Autoencoder achieved an accuracy of 94.17%, while the MLP achieved 96%. We used hyperparameters for optimization using the Gridsearch CV, resulting in a False Positive Rate (FPR) of 1.3%, outperforming the Autoencoder’s 4.1% FPR. The MLP model was further deployed to determine the legitimacy of websites based on input URLs, demonstrating its usability in real-world scenarios. This research contributes to the development of effective phishing detection models, emphasizing the importance of optimizing neural network architecture for improved accuracy and reduced false positives