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A New System for Clustering and Classification of Intrusion Detection System Alerts Using Self-Organizing Maps
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 International Journal of Computer Science and Security (IJCSS)
Table of Contents
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Volume:  4    Issue:  6
Pages:  497-610
Publication Date:   January / February
ISSN (Online): 1985-1553
Pages 
 589 - 597
Author(s)  
  
Published Date   
 08-02-2011 
Publisher 
 CSC Journals, Kuala Lumpur, Malaysia
ADDITIONAL INFORMATION
Keywords   Abstract   References   Cited by   Related Articles   Collaborative Colleague
 
KEYWORDS:   IDS, alert clustering, SOM, false positive alert reduction, alert classification 
 
 
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Intrusion Detection Systems (IDS) allow to protect systems used by organizations against threats that emerges network connectivity by increasing. The main drawbacks of IDS are the number of alerts generated and failing. By using Self-Organizing Map (SOM), a system is proposed to be able to classify IDS alerts and to reduce false positives alerts. Also some alert filtering and cluster merging algorithm are introduce to improve the accuracy of the proposed system. By the experimental results on DARPA KDD cup 98 the system is able to cluster and classify alerts and causes reducing false positive alerts considerably. 
 
 
 
 
 
 
 
 
 
1 Jaringan Informasi
  
 
 
Amir Azimi Alasti Ahrabi : Colleagues
Ahmad Habibizad Navin : Colleagues
Hadi Bahrbegi : Colleagues
Mir Kamal Mirnia : Colleagues
Mehdi Bahrbegi : Colleagues
Elnaz Safarzadeh : Colleagues
Ali Ebrahimi : Colleagues  
 
 
 
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