Automated Spam Email Identification Using Data Visualization and Machine Learning Techniques
Year: 2025
Type: article
Abstract: Spam emails are a critical cyber challenge as they contribute to loss of productivity and security breaches.In this paper, we introduce an Artificial Neural Network (ANN)-based spam classifier using visualizationmethods for enhanced explainability. We use the SpamAssassin datasets for preprocessing the text data,TF-IDF feature extraction, and training an ANN classifier. Our model has an accuracy of 99.91%, alongwith a near-optimal F1-score of 1.00. In addition, we present visualization methods like a confusion matrixheatmap and graphs to facilitate better user comprehension of the performance of classification. Thefindings show the possibility of ANN-based classifiers integrated with visualization methods for enhancedspam filtering. Future research will explore integrating transformer-based models like BERT for improvedspam classification accuracy.
Author: Aksa Abi Abraham, Diana Reeba Benjamin, Nilachandana T S
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6911960413
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