원문정보
보안공학연구지원센터(IJUNESST)
International Journal of u- and e- Service, Science and Technology
Vol.8 No.3
2015.03
pp.295-300
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
Considering that intrusion detection systems and anomaly clearly recognize malicious activity. Nowadays, data mining based intrusion detection systems, security and more rapidly detect attacks.Therefore,in this article we use a combination of k-means clustering algorithm and is used supervised support vector machine algorithm to find the best line separator. This is leading to the separation of normal and attack traffic.
목차
Abstract
1. Introduction
2. Intrusion Detection System
3. Clustering
3.1 Clustering with Only Link
3.2 Clustering with Full Links
3.3 Mean Link Clustering Method
3.4 Clustering Using Group Mean the Link
3.5 Clustering by the distance between
3.6. Algorithm K -Means
4. Support Vector Machine
5. Clustering Algorithms in Intrusion Detection Systems
6. The Method Proposed
7. Conclusion
References
1. Introduction
2. Intrusion Detection System
3. Clustering
3.1 Clustering with Only Link
3.2 Clustering with Full Links
3.3 Mean Link Clustering Method
3.4 Clustering Using Group Mean the Link
3.5 Clustering by the distance between
3.6. Algorithm K -Means
4. Support Vector Machine
5. Clustering Algorithms in Intrusion Detection Systems
6. The Method Proposed
7. Conclusion
References
저자정보
참고문헌
자료제공 : 네이버학술정보