A Hierarchical Intrusion Detection System Based on Machine Learning. Issue 1 (1st June 2022)

• Record Type: Journal Article
• Authors: Kong, Dehua; Peng, Sicheng; Zhai, Yihong; Liu, Zhangyuan; Zhang, Luming; Wan, Zixuan
• Is Part Of: Journal of physics. Volume 2294:Issue 1(2022)
• Publication Date: 2022-06-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2294/1/012033
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: The Intrusion detection system (IDS) is one of the most important tools for defending against abnormal flow and attack messages. Most of the existing IDSs use detection technology based on security policies, and there is a risk that it cannot be accurately analyzed and evaluated. Therefore, machine learning techniques provide a new direction for solving this problem. This paper uses and analyzes the CIC-IDS series datasets, but there is a data imbalance in this dataset. In order to solve the problem of data imbalance and reduce the accuracy of the model, this paper proposes a hierarchical detection model. Experiments have shown that the stratified detection module has good classification accuracy for attack types with a small sample size.