By Topic

Analysis of different architectures of neural networks for application in Intrusion Detection Systems

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Kukielka, P. ; Inst. of Telecommun., Warsaw Univ. of Technol., Warsaw ; Kotulski, Z.

Usually, intrusion detection systems (IDS) work using two methods of identification of attacks: by signatures that are specific defined elements of the network traffic possible to identification and by anomalies being some deviations form of the network behavior assumed as normal. In the both cases one must pre-defined the form of the signature (in the first case) and the networkpsilas normal behavior (in the second one). In this paper we propose application of neural networks (NN) as a tool for application in IDS. Such a method makes possible utilization of the NN learning property to discover new attacks, so (after the training phase) we need not deliver attackspsila definitions to the IDS. In the paper, we study usability of several NN architectures to find the most suitable for the IDS application purposes.

Published in:

Computer Science and Information Technology, 2008. IMCSIT 2008. International Multiconference on

Date of Conference:

20-22 Oct. 2008