|Table of Contents|

Intrusion Detection Based on Unsupervised Clustering Algorithm

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2009年03期
Page:
288-292
Research Field:
Publishing date:

Info

Title:
Intrusion Detection Based on Unsupervised Clustering Algorithm
Author(s):
WANG FeiQIAN Yu-wenWANG Zhi-quan
School of Automation,NUST,Nanjing 210094,China
Keywords:
intrusion detection computer crime detectors internet network security unsupervised clustering unlabeled data
PACS:
TP393.08
DOI:
-
Abstract:
An unsupervised clustering algorithm is proposed to solve the problem that most of intrusion detections based on clustering algorithm have artificial parameters.This method has no artificial parameter and is not affected by the order of data entrance.The shape of clusters is arbitrary,which can reflect the real distribution of data.By comparing the distances between unlabeled training data,the algorithm merges characters of clusters according to the characters of nearest samples.When each step of clustering is completed,the algorithm identifies the intrusion clusters by comparing the distances of clusters and calculating the rate of samples of each cluster among all samples.The identified clusters can be used in real data detection.The experimental result shows that the detection rate is 89.5% and the false alarm rate is 0.4% in detecting unknown intrusion.

References:

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Last Update: 2012-11-19