Authors:
Jinxia Wei
;
Chun Long
;
Wei Wan
;
Yurou Zhang
;
Jing Zhao
and
Guanyao Du
Affiliation:
Department of Security, Computer Network Information Center, Chinese Academy of Sciences, Beijing and China
Keyword(s):
Intrusion Detection, Random Forest (RF), Correlation Analysis, Logarithm Marginal Density Ratio.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Artificial Intelligence and Decision Support Systems
;
Computer-Supported Education
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Industrial Applications of Artificial Intelligence
;
Information Systems Analysis and Specification
;
Information Technologies Supporting Learning
;
Security
;
Security and Privacy
;
Sensor Networks
;
Signal Processing
;
Soft Computing
Abstract:
Intrusion detection systems are essential in the field of network security. To improve the performance of detection model, many machine learning algorithms have been applied to intrusion detection models. Higher-quality data is critical to the accuracy of detection model and could greatly improve the performance. In this paper, an effective random forest-based intrusion detection algorithm with feature reduction and transformation is proposed. Specifically, we implement the correlation analysis and logarithm marginal density ratio to reduce and strengthen the original features respectively, which can greatly improve accuracy rate of classifier. The proposed classification system was deployed on NSL-KDD dataset. The experimental results show that this paper achieves better results than other related methods in terms of false alarm rate, accuracy, detection rate and running time.