Earticle

초록

영어

The computer system is becoming more complex and massive network data, which brings great difficulties to the traditional intrusion detection system. Intrusion detection system is an important part of the network and information security architecture, which is mainly used to distinguish the normal activities of the system and the suspicious and intrusion patterns. But the challenge is how to effectively detect network intrusion behavior in order to reduce the false alarm rate and false negative rate. Based on the shortcomings of existing intrusion detection methods, the fuzzy C- means clustering method is proposed to analyze the intrusion detection data, so as to find out the abnormal network behavior patterns. By testing the CUP99 data set, the results show that the IFCA is not only feasible but also accurate and efficient. The improved fuzzy clustering algorithm proposed in this paper can improve the detection rate of intrusion detection and reduce the false detection rate, and can be widely used in intrusion detection system.

목차

Abstract
 1. Introduction
 2. The Principle of FCM Clustering
  2.1. Fuzzy C- mean Method
  2.2. Fuzzy C- mean Algorithm
  2.3. Determination of Optimal Weight Index m*
  2.4. Relative State Characteristic Value
 3. Intrusion Detection Technology
  3.1. Intrusion Detection System
  3.2. Intrusion Detection Technology Research
 4. Application of Improved Fuzzy Algorithm in Data Intrusion Detection
  4.1. Experimental Data
  4.2. Anomaly Detection
  4.3. Misuse Detection
 5. Conclusions
 References

키워드

저자

• Sheng Zhao [ Xingtai Polytechnic College, Xingtai, Hebei, China ]
• Huishan Han [ Xingtai Polytechnic College, Xingtai, Hebei, China ]
• Xuekui Shi [ Xingtai Polytechnic College, Xingtai, Hebei, 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