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Machine learning, statistical-based, and knowledge-based methods are oftenused to implement an Anomaly-based Intrusion Detection System which is softwarethat helps in detecting malicious and undesired activities in the networkprimarily through the Internet. Machine learning comprises Supervised,Semi-Supervised, and Unsupervised Learning algorithms. Supervised machinelearning uses a trained label dataset. This paper uses four supervised learningalgorithms Random Forest, XGBoost, K-Nearest Neighbours, and Artificial NeuralNetwork to test the performance of the public dataset. Based on the predictionaccuracy rate, the results show that Random Forest performs better onmulti-class Intrusion Detection System, followed by XGBoost, K-NearestNeighbours respective, provided prediction accuracy is taken into perspective.Otherwise, K-Nearest Neighbours was the best performer considering the time oftraining as the metric. It concludes that Random Forest is the best-supervisedmachine learning for Intrusion Detection System

Testing the performance of Multi-class IDS public dataset using Supervised Machine Learning Algorithms