Phishing Email Detection using a Hybrid CNN-MLP Deep Learning Framework

Abstract

Phishing incidents remain highly frequent and destructive cyber threats affecting private users and organizations globally. Malicious actors continuously refine their deceptive strategies to evade traditional rule-based filters and conventional machine learning models, leading to financial losses, credential theft, and data breaches. This study presents an enhanced phishing email detection system based on a hybrid deep learning framework integrating a Convolutional Neural Network (CNN) with a Multi-Layer Perceptron (MLP). The CNN component automatically extracts discriminative textual features from email content, while the MLP component performs accurate email classification as legitimate or phishing. A publicly available phishing email dataset from the Kaggle platform was utilized for model training and evaluation. The dataset underwent preprocessing stages including data cleaning, text normalization, tokenization, sequence padding, and label encoding. The CNN layer extracted relevant textual patterns and semantic representations, while the MLP network performed final classification. The developed system was evaluated using accuracy, precision, recall, and F1-score. Experimental results demonstrated that the CNN–MLP model achieved 98.5% accuracy, 98% precision, 99% recall, and 99% F1-score, indicating strong capability in distinguishing phishing from legitimate emails. Additionally, a web-based application was developed to facilitate real-time phishing detection using direct text input and PDF document analysis through Optical Character Recognition (OCR). The findings demonstrate that integrating CNN-based feature extraction with MLP-based classification provides an effective, reliable, and scalable solution for phishing email detection, contributing to improved cybersecurity protection against evolving phishing attacks.