Research Article
Advanced Email Bot Detection: Threat Analysis and Mitigation Strategies
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 186 - Issue 70 |
| Published: March 2025 |
| Authors: Rahul Goel |
10.5120/ijca2025924555
|
Rahul Goel . Advanced Email Bot Detection: Threat Analysis and Mitigation Strategies. International Journal of Computer Applications. 186, 70 (March 2025), 32-34. DOI=10.5120/ijca2025924555
@article{ 10.5120/ijca2025924555,
author = { Rahul Goel },
title = { Advanced Email Bot Detection: Threat Analysis and Mitigation Strategies },
journal = { International Journal of Computer Applications },
year = { 2025 },
volume = { 186 },
number = { 70 },
pages = { 32-34 },
doi = { 10.5120/ijca2025924555 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2025
%A Rahul Goel
%T Advanced Email Bot Detection: Threat Analysis and Mitigation Strategies%T
%J International Journal of Computer Applications
%V 186
%N 70
%P 32-34
%R 10.5120/ijca2025924555
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Email bots pose a significant threat to individuals and organizations, as they can be used to spread spam, malware, and phishing attacks. Detecting these bots is crucial for maintaining the security and integrity of email communication. This paper provides a comprehensive overview of email bot detection, including the types of bots, their characteristics, and the techniques used to identify them. Additionally, an extensive evaluation of bot detection methodologies is presented, supported by graphical and tabular data. The research also addresses challenges associated with bot detection and explores future directions in this field.
References
- Comprehensive Study of Email Spam Botnet Detection. ResearchGate
- The False Positive Problem in Automatic Bot Detection. PMC.
- SEON Bot Detection Techniques. SEON.io.
- Identifying Bot Clicks and Spam Filter Activity. Marketing Nation.
- Study on the Accuracy of Bot Detection Software. MIT Sloan.
- Transformer-Based Models for Detecting Phishing Emails. Journal of Machine Learning Research.
- Deep Learning Approaches for Bot Detection in Emails. IEEE Transactions on Information Security.
Index Terms
Keywords