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

Le résumé fourni par la source

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.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.