Analysis of NGINX Traffic Logs Using Machine Learning

Keywords: log analysis, NGINX, machine learning, data analysis, anomaly detection, web server security, data processing and analysis

Abstract

This article discusses the process of using machine learning methods for analyzing NGINX web server logs. The main focus is on how to apply machine learning algorithms for effective detection of anomalies, user behavior patterns, and potential security threats. Approaches to collecting, processing, and analyzing log data are considered, as well as using this data to improve the security and efficiency of web servers.

References

1. Overview: Metrics and Metadata | NGINX Documentation. NGINX Documentation.
2. Machine Learning Algorithms - Javatpoint. www.javatpoint.com.
3. Documentation | Terraform | HashiCorp Developer. Documentation | Terraform | HashiCorp Developer.
4. pandas documentation – pandas 2.1.3 documentation. pandas - Python Data Analysis Library.
5. Crontab(5) - Linux manual page. Michael Kerrisk - man7.org.

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Published
2023-12-16
How to Cite
Bahnіuk N., Linchuk, O., & Shypulin , O. (2023). Analysis of NGINX Traffic Logs Using Machine Learning. COMPUTER-INTEGRATED TECHNOLOGIES: EDUCATION, SCIENCE, PRODUCTION, (53), 86-91. https://doi.org/10.36910/6775-2524-0560-2023-53-13
Section
Computer science and computer engineering