Exploring the Current State of Machine Learning in Spam Filters

Sizhe Teng

2025

Abstract

This paper systematically analyzes the development of spam detection technology. Spam poses a significant threat to network security, personal privacy, and enterprise productivity. Traditional filtering methods, such as rule-based filtering and Bayesian classification, have difficulty adapting and coping with evolving spam strategies. This paper focuses on machine learning and evaluates its adaptability, feature extraction capabilities, and algorithmic effectiveness in enhancing spam detection. By analyzing algorithms such as Naive Bayes, Random Forest, and LSTM, this study highlights the improvements in adaptability and accuracy brought by machine learning. However, existing challenges include dependence on labelled datasets and computing resources. This study provides theoretical and practical insights for building adaptive spam detection systems. This study not only provides theoretical support for the development of spam filtering technology but also provides practical references for enterprises and individuals to build efficient and intelligent spam defense systems in practical applications, which helps to improve email security, which is crucial to maintaining trust in the digital economy.

Paper Citation

in Harvard Style

Teng S. (2025). Exploring the Current State of Machine Learning in Spam Filters. In Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE; ISBN 978-989-758-765-8, SciTePress, pages 555-559. DOI: 10.5220/0013701900004670

in Bibtex Style

@conference{icdse25,
author={Sizhe Teng},
title={Exploring the Current State of Machine Learning in Spam Filters},
booktitle={Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE},
year={2025},
pages={555-559},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013701900004670},
isbn={978-989-758-765-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd International Conference on Data Science and Engineering - Volume 1: ICDSE
TI - Exploring the Current State of Machine Learning in Spam Filters
SN - 978-989-758-765-8
AU - Teng S.
PY - 2025
SP - 555
EP - 559
DO - 10.5220/0013701900004670
PB - SciTePress