Testing the performance of Multi-class IDS public dataset using Supervised Machine Learning Algorithms. (arXiv:2302.14374v1 [cs.CR])

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 …

algorithms artificial class detection forest ids internet intrusion intrusion detection intrusion detection system knowledge machine machine learning machine learning algorithms malicious network performance public random software system testing unsupervised learning