Earticle

초록

영어

Intrusion detection is an important way to ensure the security of computers and networks. In this paper, a new intrusion detection system (IDS) is proposed based on Hidden Conditional Random Fields (HCRFs). In order to optimize the performance of HCRFs, we bring forward the Two-stage Feature Selection method, which contains Manual Feature Selection method and Backward Feature Elimination Wrapper (BFEW) method. The BFEW is a feature selection method which is introduced based on wrapper approach. Experimental results on KDD99 dataset show that the proposed IDS not only has a great advantage in detection efficiency but also has a higher accuracy when compared with other well-known methods.

목차

Abstract
 1. Introduction
 2. Related Works
 3. Hidden Conditional Random Fields for Intrusion Detection
 4. Two-stage Feature Selection
  4.1. Manual Feature Selection
  4.2. Backward Feature Elimination Wrapper
 5. Framework of the IDS
 6. Experiments and Results
  6.1. Feature Selection with BFEW
  6.2. Detection Models Building for Each Layer
  6.3. The Overall IDS Performance
  6.4. Comparison with Other IDSs
 7. Conclusion and Future Works
 Acknowledgments
 References

키워드

저자

• Jun Luo [ Key Laboratory of Optoelectronic Technology & System, Ministry of Education, Chongqing University, Chongqing 400030, China ]
• Zenghui Gao [ Key Laboratory of Optoelectronic Technology & System, Ministry of Education, Chongqing University, Chongqing 400030, China ]

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