Earticle

초록

영어

As network techniques have become one of the most significant aspects of our daily lives, network security has been a major concern. One common application is network intrusion detection. From the perspective of data oriented consideration, intrusion detection can be formulated as a clustering task, which aims to differentiate normal and insecurity behaviors and categorize into several groups. In this paper, we employ ensemble clustering method to improve the generalization and robustness of basic clustering. Specifically, we employ fuzzy kernel C-means (FKCM) as basic clustering, which improves the fuzzy C-means (FCM) clustering by introducing kernels from the support vector machines (SVM) to optimize the features of sample data by mapping the sample pattern into a higher dimensional feature space. Then, we formulate the ensemble problem as the optimization of the mutual information among all clusterings and introduce Ant Colony Optimization (ACO) as the solution. Experiments prove the efficiency of our method.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Preliminaries
 4. Proposed Intrusion Detection Model
  4.1 Basic Clustering Using FKCM
  4.2 Clustering Ensemble with ACO
 5. Experiment
 6. Conclusion
 References

키워드

저자

• Liang-Wei Chen [ Department of information and Engineering, Chengdu Aeronautic Polytechnic, Chengdu, Sichuan, 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