A Critical Review - Use of Ensemble Methods in Intrusion Detection System
Keywords:
Intrusion Detection, Behavior-Based IDS, Ensemble Learning, and Classification.Abstract
IDSs are essential to the security of contemporary ICT systems. IDSs detect and report attacks, which are frequently examined by administrators tasked with thwarting the assault and reducing damage. As a result, it's critical that the IDS's alerts are as thorough as they can be. In this study paper has offered a multi-layered behavior-based IDS that classifies network using ensemble learning approaches. The ensemble has been built using Decision Trees, NB, SVM and Random Forests, these popular and well-liked models. Our solution is made to rapidly filter away traffic that has been identified as benign without further research in order to speed up system response time, while suspicious events are looked into to produce a more precise categorization. According to experimental setup has discussed on the various public datasets, the system can detect nine forms of high performances across all parameters taken into consideration.
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