원문정보
보안공학연구지원센터(IJSIA)
International Journal of Security and Its Applications
Vol.8 No.5
2014.09
pp.311-322
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
In this paper, we propose a multi-layer selection and mining methods for effective intrusion detection, which utilize feature selection, classification, clustering and evidence theory for decision making. In the experiments, DARPA KDD-99 intrusion detection data set is used for evaluation. It shows that our proposed classifier not only classifies and separates the normal and abnormal data, but also reduces false positive and false negative besides detecting all four kinds of attacks.
목차
Abstract
1. Introduction
2. Related Works
3. Our Proposed Approach
3.1. Classifier
3.2. Clustering and Classification Method
3.3. Data Mining based Classifier
3.4. Evidence Theory based Combiner
4. Experimental Evaluation
4.1. Data Set Introduction
4.2. Experimental Results
4.3. Discussions
5. Conclusions
References
1. Introduction
2. Related Works
3. Our Proposed Approach
3.1. Classifier
3.2. Clustering and Classification Method
3.3. Data Mining based Classifier
3.4. Evidence Theory based Combiner
4. Experimental Evaluation
4.1. Data Set Introduction
4.2. Experimental Results
4.3. Discussions
5. Conclusions
References
키워드
저자정보
참고문헌
자료제공 : 네이버학술정보