원문정보
초록
영어
Intrusion detection is the means to identify the intrusive behaviors and provide useful information to intruded systems to respond fast and to avoid or reduce damages. In recent years, learning machine technology is often used as a detection method in anomaly detection. In this research, we use support vector machine as a learning method for anomaly detection, and use LibSVM as the support vector machine tool. By using this tool, we get rid of numerous and complex operations and do not have to use external tools for finding parameters as needed by using other algorithms such as the genetic algorithm. Experimental results show that high average detection rates and low average false positive rates in anomaly detection are achieved by our proposed approach.
목차
1. Introduction
2. SVM
2.1. C-SVM
2.2. One-class SVM
2.3. LibSVM
3. Experiment Setup
3.1. Data Source
3.2. Experimental Environment
3.3. Data Processing
3.4. Data Feature
4. Experiment Processes and Results
5. Conclusions and future works
6. Acknowledgement
7. References