원문정보
보안공학연구지원센터(IJDTA)
International Journal of Database Theory and Application
Vol.7 No.2
2014.04
pp.59-70
피인용수 : 0건 (자료제공 : 네이버학술정보)
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
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
저자정보
참고문헌
자료제공 : 네이버학술정보