Earticle

초록

영어

Nowadays, much attention has been paid to intrusion detection system (IDS) which is closely linked to the safe use of network services. Several machine-learning paradigms including neural networks, linear genetic programming (LGP), support vector machines (SVM), Bayesian networks, multivariate adaptive regression splines (MARS) fuzzy inference systems (FISs), etc. have been investigated for the design of IDS. In this paper, we develop a hybrid method of C5.0 and SVM and investigate and evaluate the performance of our proposed method with DARPA dataset. The motivation for using the hybrid approach is to improve the accuracy of the intrusion detection system when compared to using individual SVM and individual SVM.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Proposed Method
  3.1. SVM Classifier
  3.2. C5.0 Algorithm
  3.3. Hybrid Decision tree–SVM (DT– SVM) Approach
 4. Experiments
  4.1. Data Set and Evaluation Criteria
  4.2. Quality of Classification
 5. Conclusions
 References

키워드

저자

• Vahid Golmah [ of Computer Engineering, Neyshabur Branch, , Islamic Azad University,Neyshabur, Iran ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605