King Saud University Repository >
King Saud University >
Science Colleges >
College of Computer and Information Sciences >
College of Computer and Information Sciences >

Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/15714

Title: Remote to Local attack detection using supervised neural network
Authors: Iftikhar Ahmad
Azween B. Abdullah
Abdullah S. Alghamdi
Keywords: Kddcup99 , artificial neural network , backpropagation algorithm , feedforward neural network , remote to local attack detection , supervised neural network
Issue Date: 2010
Abstract: In order to determine Remote to Local (R2L) attack, an intrusion detection technique based on artificial neural network is presented This technique uses sampled dataset from Kddcup99 that is standard for benchmarking of attack detection tools. The back propagation algorithm is used for training the feedforward neural network. The developed system is applied to R2L attacks. Moreover, experiment indicates this technique has comparatively low false positive rate and false negative rate, consequently it effectively resolves the deficiency of existing intrusion detection approaches.
URI: http://hdl.handle.net/123456789/15714
Appears in Collections:College of Computer and Information Sciences

Files in This Item:

File Description SizeFormat
Lct.Iftikhar_Conf_7.docx15 kBMicrosoft Word XMLView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.


DSpace Software Copyright © 2002-2009 MIT and Hewlett-Packard - Feedback