DocumentCode
2300179
Title
Intrusion Detection System Using Self-Organizing Maps
Author
Alsulaiman, Mansour M. ; Alyahya, Aasem N. ; Alkharboush, Raed A. ; Alghafis, Nasser S.
Author_Institution
Coll. of Comput. & Inf. Sci., King Saud Univ., Riyadh, Saudi Arabia
fYear
2009
fDate
19-21 Oct. 2009
Firstpage
397
Lastpage
402
Abstract
Internet became one of life´s basics in these days. More networks are connected to the Internet every day, which increases the amount of valuable data and the number of resources that can be attacked. Some systems have been designed and developed to secure these data and prevent attacks on resources. Unfortunately, new attacks are being created everyday, which make it hard to design a system that will catch these attacks. The need is not only for preventing the attack but also for detecting such attacks if it happened. Intrusion detection systems is built to do this task and complement other security systems. In this paper we build an intrusion detection system using a well known unsupervised neural network, namely Kohonen maps. We present the solution by some researchers then propose two enhancements. The enhancement we did gave good result and was able to solve some off the shortcomings of available solution, namely high value of false positive.
Keywords
security of data; self-organising feature maps; unsupervised learning; Internet; Kohonen map; high value-of-false positive; intrusion detection system; neural network; self-organizing map; unsupervised learning algorithm; Computer networks; Computer security; Data security; Educational institutions; Information analysis; Information security; Intrusion detection; Neural networks; Self organizing feature maps; Telecommunication traffic; Detection system; Intrusion; Intrusion Detection system; hierarchical SOM; self organizing maps;
fLanguage
English
Publisher
ieee
Conference_Titel
Network and System Security, 2009. NSS '09. Third International Conference on
Conference_Location
Gold Coast, QLD
Print_ISBN
978-1-4244-5087-9
Electronic_ISBN
978-0-7695-3838-9
Type
conf
DOI
10.1109/NSS.2009.62
Filename
5319303
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