Testing the performance of Multi-class IDS public dataset using Supervised Machine Learning Algorithms
Machine learning, statistical-based, and knowledge-based methods are often
used to implement an Anomaly-based Intrusion Detection System which is software
that helps in detecting malicious and undesired activities in the network
primarily through the Internet. Machine learning comprises Supervised,
Semi-Supervised, and Unsupervised Learning algorithms. Supervised machine
learning uses a trained label dataset. This paper uses four supervised learning
algorithms Random Forest, XGBoost, K-Nearest Neighbours, and Artificial Neural
Network to test the performance of the public dataset. Based on the prediction
accuracy rate, the results show that Random Forest performs better on
multi-class Intrusion Detection System, followed by XGBoost, K-Nearest
Neighbours respective, provided prediction accuracy is taken into perspective.
Otherwise, K-Nearest Neighbours was the best performer considering the time of
training as the metric. It concludes that Random Forest is the best-supervised
machine learning for Intrusion Detection System