Paper Info
Title
Threshold-Based Clustering For Intrusion Detection Systems
Abstract
Signature-based intrusion detection systems look for known, suspicious patterns in the input data. In this paper we explore compression of labeled empirical data using threshold-based clustering with regularization. The main target of clustering is to compress training dataset to the limited number of signatures, and to minimize the number of comparisons that are necessary to determine the status of the input event as a result. Essentially, the process of clustering includes merging of the clusters which are close enough. As a consequence, we will reduce original dataset to the limited number of labeled centroids. In a complex with k-nearest-neighbor (kNN) method, this set of centroids may be used as a multiclass classifier. Clearly, different attributes have different importance depending on the particular training database. This importance may be regulated in the definition of the distance using linear weight coefficients. The paper introduces special procedure to estimate above weight coefficients.The experiments on the KDD-99 intrusion detection dataset have confirmed effectiveness of the proposed methods.
Year
DOI
Venue
2006
10.1117/12.665326
DATA MINING, INTRUSION DETECTION, INFORMATION ASSURANCE, AND DATA NETWORKS SECURITY 2006
Keywords
Field
DocType
distance-based clustering, k-nearest-neighbor method, intrusion detection
Data mining,Fuzzy clustering,Pattern recognition,Computer science,Regularization (mathematics),Artificial intelligence,Cluster analysis,Data compression,Classifier (linguistics),Merge (version control),Intrusion detection system,Centroid
Conference
Volume
ISSN
Citations 
6241
0277-786X
1
PageRank 
References 
Authors
0.36
12
1
Name
Order
Citations
PageRank
Vladimir Nikulin19917.28