Comparison and analysis of supervised machine learning algorithms

Abstract

When investigating a network for signs of infiltration, intrusion detection is used. An intrusion detection system is designed to prevent unwanted access to the system. Data mining techniques have been employed by a number of researchers to detect infiltrations in this field. Based on distance measurements, this study proposes algorithms for supervised machine learning. In terms of detection rate, accuracy, false alarm rate, and Matthews correlation coefficient, supervised machine learning techniques surpass other algorithms. When it comes to serial execution time, the supervised machine learning algorithms surpassed all other Actions in terms of serial execution performance.