Vol. 5 No. 1 (2025): Issue 5
Articles

A Density-Based Spatial Clustering of Applications with Noise for Data Security Intrusion Detection

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  • Carlos Méndez,
  • Laura García,
  • Javier Torres
DOI: https://doi.org/10.71070/oaml.v5i1.44

Published 2025-01-21

Keywords

  • Data Security,
  • Intrusion Detection,
  • Density-Based Clustering,
  • Anomaly Detection,
  • Research Contribution

How to Cite

Méndez, C., García, L., & Torres, J. (2025). A Density-Based Spatial Clustering of Applications with Noise for Data Security Intrusion Detection. Optimizations in Applied Machine Learning, 5(1). https://doi.org/10.71070/oaml.v5i1.44
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Abstract

In the field of data security intrusion detection, the challenge lies in effectively identifying and categorizing intrusion activities amidst the influx of data. Current research predominantly focuses on traditional methods that may overlook subtle yet significant patterns, thereby hindering accurate intrusion detection. This paper addresses this gap by proposing a novel approach - A Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for Data Security Intrusion Detection. By harnessing the power of density-based clustering, our method enhances the detection capability by capturing intricate relationships and anomalies within the data. Through extensive experimentation and analysis, we demonstrate the effectiveness and reliability of our approach in improving the accuracy and efficiency of intrusion detection systems. This innovative contribution not only enriches the existing research landscape but also paves the way for enhanced data security measures in the digital era.

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