Journal article
An Improved Intrusion Detection based on Neural Network and Fuzzy Algorithm
Journal of networks, v 9(5), pp 1274-1274
01 May 2014
Abstract
The computer network security plays an important role in modern computer systems with the widespread use of network. Intrusion diction technology has become very important in the area of network security research. Performance of common intrusion detection system is low and error detection rate is high. In order to overcome the current high rate of false positives, low detection rate and other defects of intrusion detection system, a new detection algorithm-T-S FNN based network intrusion detection algorithm is presented. This algorithm uses T-S FNN to classify objects, divides eigen-space of objects and recognizes normal behaviors and intrusions. The results of experiment show that the new method is feasible, effective and extensible. The false detection rate is reduced and the rate of correct detection is raised to a certain extent too.
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21 citations in Scopus
Details
- Title
- An Improved Intrusion Detection based on Neural Network and Fuzzy Algorithm
- Creators
- He Liang
- Publication Details
- Journal of networks, v 9(5), pp 1274-1274
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Scopus ID
- 2-s2.0-84900309155
- Other Identifier
- 991019320708904721