Application of machine learning algorithm in intrusion detection technology
Tianyu Zhang, Ming Song, Qiang Zhang, Suwen Li · 2023
Intrusion Detection (ID), as an effective network security protection technology, is an important means to ensure network security. In order to solve the problem that traditional intrusion detection technology is difficult to adapt to the current complex and changeable new network attacks, this paper uses the Random Forest (RF) algorithm of machine learning algorithm to solve this problem. Random forest algorithm has good feature learning and classification ability, which can effectively improve the accuracy of intrusion detection system. However, when the characteristic values of the sample data are high, its time complexity will also be high. In this paper, K-means clustering algorithm and One-Rule algorithm are introduced to improve the random forest algorithm, improve its modeling and classification speed, and further improve the detection efficiency by means of hierarchical division. In this paper, the proposed KOne-RF algorithm is verified by using NSL-KDD data set. Experimental results show that compared with the traditional RF algorithm, KOne-RF algorithm has improved modeling time, detection classification and detection accuracy, and detection ability for different types of attack behavior, which verifies the reliability of KOne-RF algorithm.