Experimental Performance Evaluation of an Enhanced Deep Neural Network Model for Phishing Intrusion Detection in Web-Enabled Financial Information Systems
Year: 2025
Type: article
Abstract: This study proposes an enhanced Deep Neural Network (Deep-NN) model for phishing intrusion detection in web-enabled financial information systems. The approach combines Principal Component Analysis (PCA) for feature optimization with both supervised and unsupervised learning techniques to improve detection accuracy and efficiency. Data pre-processing and dimensionality reduction were implemented in Python (Spyder IDE), while MATLAB was used for model training, validation, and testing. Performance was evaluated using regression (R), mean square error (MSE), and Jaccard similarity index. Results show a 90% classification precision with a 10% error rate, outperforming existing models in the literature. The findings demonstrate the model’s potential to strengthen cyber defense mechanisms in financial systems through robust and adaptive phishing detection.
Authors: Allen, Akinkitan Ajose, Akinola, Solomon Olalekan
Institutions: Lead City University, University of Ibadan
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4414029816