Deciphering Spam Through AI: From Traditional Methods to Deep Learning Advancements in Email Security

Xu Liu

2024

Abstract

Spam email detection received much attention in the last decades. This paper presents a comprehensive review of the evolving role of Artificial Intelligence (AI) in combating email spam, tracing the journey from traditional machine learning algorithms to sophisticated deep learning approaches. It meticulously examines the frameworks for machine learning-based spam detection, highlighting the transition from Support Vector Machines (SVM), Random Forests, and K-Nearest Neighbors (KNN) to advanced methodologies like Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). The study also delves into the challenges of interpretability, data diversity, and computational demands associated with deep learning models, and suggests future directions including the use of interpretable AI models and advanced algorithms for improved adaptability. By synthesizing recent advancements and identifying avenues for future research, this paper aims to contribute to the ongoing discourse on AI's potential to enhance email security against spam, offering insights into both the achievements and hurdles in the field.

Paper Citation

in Harvard Style

Liu X. (2024). Deciphering Spam Through AI: From Traditional Methods to Deep Learning Advancements in Email Security. In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-713-9, SciTePress, pages 553-558. DOI: 10.5220/0012958700004508

in Bibtex Style

@conference{emiti24,
author={Xu Liu},
title={Deciphering Spam Through AI: From Traditional Methods to Deep Learning Advancements in Email Security},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={553-558},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012958700004508},
isbn={978-989-758-713-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - Deciphering Spam Through AI: From Traditional Methods to Deep Learning Advancements in Email Security
SN - 978-989-758-713-9
AU - Liu X.
PY - 2024
SP - 553
EP - 558
DO - 10.5220/0012958700004508
PB - SciTePress