Building an Effective Intrusion Detection System by Using Hybrid Data Optimization Based on Machine Learning Algorithms
Joint Authors
Hu, Jing-jing
Ren, Jiadong
Guo, Jiawei
Qian, Wang
Yuan, Huang
Hao, Xiaobing
Source
Security and Communication Networks
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-06-16
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Information Technology and Computer Science
Abstract EN
Intrusion detection system (IDS) can effectively identify anomaly behaviors in the network; however, it still has low detection rate and high false alarm rate especially for anomalies with fewer records.
In this paper, we propose an effective IDS by using hybrid data optimization which consists of two parts: data sampling and feature selection, called DO_IDS.
In data sampling, the Isolation Forest (iForest) is used to eliminate outliers, genetic algorithm (GA) to optimize the sampling ratio, and the Random Forest (RF) classifier as the evaluation criteria to obtain the optimal training dataset.
In feature selection, GA and RF are used again to obtain the optimal feature subset.
Finally, an intrusion detection system based on RF is built using the optimal training dataset obtained by data sampling and the features selected by feature selection.
The experiment will be carried out on the UNSW-NB15 dataset.
Compared with other algorithms, the model has obvious advantages in detecting rare anomaly behaviors.
American Psychological Association (APA)
Ren, Jiadong& Guo, Jiawei& Qian, Wang& Yuan, Huang& Hao, Xiaobing& Hu, Jing-jing. 2019. Building an Effective Intrusion Detection System by Using Hybrid Data Optimization Based on Machine Learning Algorithms. Security and Communication Networks،Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1210542
Modern Language Association (MLA)
Ren, Jiadong…[et al.]. Building an Effective Intrusion Detection System by Using Hybrid Data Optimization Based on Machine Learning Algorithms. Security and Communication Networks No. 2019 (2019), pp.1-11.
https://search.emarefa.net/detail/BIM-1210542
American Medical Association (AMA)
Ren, Jiadong& Guo, Jiawei& Qian, Wang& Yuan, Huang& Hao, Xiaobing& Hu, Jing-jing. Building an Effective Intrusion Detection System by Using Hybrid Data Optimization Based on Machine Learning Algorithms. Security and Communication Networks. 2019. Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1210542
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1210542