JOURNAL ARTICLE

Application of Network Intrusion Detection Based on Fuzzy C-Means Clustering Algorithm

Abstract

Aiming at the problem of higher false positive and missing report rate in network intrusion detection, an intrusion detection method based on clustering algorithm is proposed in this paper. This method applies Fuzzy C-means clustering Algorithm to the detection of network intrusion. Through the building of intrusion detection model, carries out fuzzy partition and the clustering of data, and this will detach normal data and attack data effectively. The experiment shows the feasibility and validity of Fuzzy C-means clustering algorithm.

Keywords:
Cluster analysis Intrusion detection system Data mining Computer science Fuzzy clustering Fuzzy logic Partition (number theory) Intrusion Algorithm Canopy clustering algorithm Artificial intelligence Pattern recognition (psychology) Mathematics

Metrics

20
Cited By
0.69
FWCI (Field Weighted Citation Impact)
5
Refs
0.76
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Network Security and Intrusion Detection
Physical Sciences →  Computer Science →  Computer Networks and Communications
Anomaly Detection Techniques and Applications
Physical Sciences →  Computer Science →  Artificial Intelligence
Data Mining Algorithms and Applications
Physical Sciences →  Computer Science →  Information Systems

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