"Analysis of K- Nearest Neighbor for Network Intrusion Detection"
Mba Obasi Odim, Shalom Oluwapelumi Ojo, Bosede Oyenike Oguntunde, Toluwase Ayobami Olowookere · International Journal of Software & Hardware Research in Engineering · 2023
Computer network Intrusion is an unauthorised action or activity on a network.The threat of intrusions on cyber-security has grown significantly recently.Various techniques are used to counteract these dangers with some levels of detection accuracy.This study models and assesses the performance of k-Nearest Neighbor (KNN) for intrusion detection.An online intrusion detection dataset of National State and Local Knowledge Discovery in Database (NSL-KDD) from Kaggle was used for the assessment comprising 43 features and instances of both normal and abnormal data streams.The model is implemented in python and assessed using Precision, Recall, and F1 score.The assessment results show 99.996% accuracy and 1.00 respectively for Precision, Recall and F1 Score for the 2-class classification (normal and abnormal) and 99.988% and also 1.00 respectively for Precision, Recall and F1 Score for each of the abnormal multi-class classification (Denial of service, Remote to user Attack, User to root, and probe), except for the User to root class that records a Precision of 9.99.These results suggest that KNN is an effective algorithm for intrusion detection both for the binary and multi class classification.and, therefore, should be adopted for developing an intrusion detection system.