Earticle

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

Abstract
 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

저자

• Jung-Chun Liu [ Department of Computer Science and Information Engineering, Tunghai University, Taiwan ]
• Chu-Hsing Lin [ Department of Computer Science and Information Engineering, Tunghai University, Taiwan ]
• Jui-Ling Yu [ Department of Applied Mathematics, Providence University, Taiwan ]
• Wei-Shen Lai [ Department of Information Management, Chienkuo Technology University, Taiwan ]
• Chia-Han Ho [ Department of Computer Science and Information Engineering, Tunghai University, Taiwan ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605