An Improved Convolutional Neural Network-Based Spam Recognition Model

DOI

10.2991/978-94-6463-540-9_66How to use a DOI?

Keywords

Convolutional Neural Networks; Spam Recognition; Cyber Security

Abstract

Spam is one of the significant threats to cyber security by not only sending unwanted messages but also by potentially carrying viruses. Conventional spam detection methods, such as keyword matching and rule-based filtering, are less effective since spammers could advance their method to bypass these simple detection approaches. Machine learning algorithms can quickly and effectively identify subtle relationships among email texts, thus providing a more promising defense against spams. In this work, a Convolutional Neural Network (CNN)-based approach to spam recognition, leveraging the power of deep learning to process and analyze email content. This method is designed to address the shortcomings of traditional methods by employing a deep learning architecture that can generalize well to new, unseen data. Through detailed experimental analysis, the paper demonstrates that the proposed model not only achieves high performance in detecting spam but also significantly reduces the incidence of false positives, which is crucial for maintaining user trust and ensuring that normal emails will not be wrongly classified as spam.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Jinyuan Liu
PY  - 2024
DA  - 2024/10/16
TI  - An Improved Convolutional Neural Network-Based Spam Recognition Model
BT  - Proceedings of the 2024 2nd International Conference on Image, Algorithms and Artificial Intelligence (ICIAAI 2024)
PB  - Atlantis Press
SP  - 647
EP  - 655
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-540-9_66
DO  - 10.2991/978-94-6463-540-9_66
ID  - Liu2024
ER  -