Abstract

Digital communication and cybersecurity are major problems concerning the spread of unsolicited and malicious emails. Spam emails are vectors of phishing, malware and financial fraud as well as inbox clutter. The conventional spam detection methods, like rule-based spam filters and the classical machine learning (ML) models, have limitations on their ability to detect spam based on pre-determined patterns, feature engineering, which requires a lot of human effort and inability to adapt to changes in threats. To address these, the given piece uses a deep learning-driven framework that would allow the extraction of complex and hierarchical patterns in email data automatically. Model training and evaluation are performed using the Spam Base benchmark dataset comprising 4601 emails having 57 features. High-quality input data can be guaranteed by doing comprehensive preprocessing such as parsing, tokenization, stemming, case folding, error correction, and extraction with the help of regressions. Feature extraction, dimensionality reduction, and data classification selection techniques are used to further optimize the dataset. Accuracy, Recall, Precision, and F1-score were evaluated using a 70:30 train-test split for an Artificial Neural Network (ANN), with results of 99.50, 99.68, 99.68, and 99.68, respectively. The findings prove the strength, scalability, and usefulness of the framework in spam detection, which helps to increase cybersecurity and efficient email communication systems. Further improvements on the detection of advanced spamming can be discussed in future work, and one of the methods is the multi-mode and transformer-based approach.

Keywords

Email Spam Detection, Deep Learning, Machine Learning Cybersecurity, Malicious Email Filtering, Email Classification, Support Vector Machines (SVM), Naive Bayes (NB). Long Short-Term Memory (LSTM).

Conclusion and Future Work

The deep learning-based model temperature in this paper illustrates a very efficient methodology to classify and detect email spam and eliminate the weaknesses of the conventional rule-based and ML methods. With the help of an ANN trained based on the Spam Base dataset, the model demonstrated very good results, with a 99.68% F1-score, 99.68% Recall, 99.68% Precision, and 99.50% Accuracy, demonstrating good performance in the field of spam and legitimate emails. The preprocessing, feature extraction, dimensionality reduction and feature selection steps made sure that the input data was of good quality so that the ANN could learn the complex patterns and dependencies that the traditional methods could have overlooked. The ANN was considered to have a better capability to reduce the false positives and negatives and at the same time balance performance across the evaluation measures when compared to the standard neural networks, MLP and LSTM models. The next step in development of model is consideration of multi-modal spam detection model which adds to the text-only model attachments, pictures, and embedded links to counter more complex threats. The addition of transformer-based architecture may also promote semantic interpretation and flexibility to the changing spam tactics. Also, the implementation of the system in the large-scale enterprise email infrastructures will test scalability, real-time performance, and robustness. Regular updates of datasets and the dynamic process of learning will assist in keeping the models relevant, which would guarantee further improvement of cybersecurity and stable email communication.

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