Earticle

초록

영어

The diversity and concealment of network attack lead to the difficulty of network intrusion detection, in order to further improve the detection accuracy and efficiency of network intrusion detection, this article proposes a novel model FS-ELM, which is based on the combination of Fisher Score (FS) for feature selection and ELM classifiers for network intrusion detection. In the proposed model, FS is used to conduct feature selection to select the most distinguished feature subsets, and then to get diverse training subsets, in terms of these subsets, ELM classifiers are trained. Finally the results are achieved. Experiment on KDD CUP 99 data set, by means of the experimental analysis and comparison with SVM, LS-SVM and KNN, the proposed model not only improves the detection accuracy, but also enhances detection efficiency, it proves that it is an effective model for network intrusion detection.

목차

Abstract
 1. Introduction
 2. Introduction to the Theory
  2.1. Fisher Score (FS)
  2.2. Extreme Learning Machine (ELM)
 3. FS-ELM Model
  3.1. The Generation of Training Subsets
  3.2. The Creation of Classifier
  3.3. The Result of Classification
 4. Experiment Design
  4.1. The Description and Pretreatment of Data
  4.2. Experiment Setting
 5. The Results and Analysis of Experiment
 6. Conclusion
 References

키워드

저자

• Hong Mei [ Changchun University, Chang Chun, China ]
• Wang Ju [ Changchun University, Chang Chun, China ]
• Qi Yao Wu [ CRRC ChangChun Railway Vehicles Co., LTD, Chang Chun, China ]
• Zhai Ning [ State Crid Jilin Electric Power Company Limited, Chang Chun, 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