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
翻译:机械学习、基于统计和知识的方法常常被用来实施以异常为基础的入侵探测系统,该系统主要是通过互联网,帮助发现网络中恶意和不理想的活动。机器学习包括监督、半超和不受监督的学习算法。受监督的机器学习使用经过培训的标签数据集。本文使用四种监督的学习算法随机森林、XGBoost、K-Nearest邻居和人工神经网络测试公共数据集的性能。根据预测准确率,结果显示随机森林在多级入侵探测系统上的表现更好,随后是XGBoost、K-Nearest邻居,但前提是能够对预测的准确性进行观察。否则,K-Nearest邻居是将培训时间视为衡量的最佳表现者。它的结论是,随机森林是入侵探测系统最受监督的机器学习。它的结论是,随机森林是入侵探测系统最受监督的机器学习。</s>