About this document
Data Mining for Intrusion Detection Systems by International Journal of computational Engineering research (IJCER) is a document available to read on EtoBox.
This document discusses using a combined approach of K-medoids clustering and Naive Bayes classification for intrusion detection. It begins with background on intrusion detection systems and data mining techniques. It then proposes using K-medoids clustering to group data before applying Naive Bayes classification. The goal is to improve accuracy and reduce false alarms. An experiment on a custom dataset shows the combined approach performs better in terms of accuracy and detection rate with a reasonable fa
- Author
- International Journal of computational Engineering research (IJCER)
- Language
- EN