DSpace

King Saud University Repository >
King Saud University >
COLLEGES >
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/123456789/15711

Title: Applying neural network to U2R attacks
Authors: Iftikhar Ahmad
Azween B. Abdullah
Abdullah S. Alghamdi
Keywords: Backpropagation , Dataset , Detection Rate , False Negative , False Positive , Multiple Layered Perceptron , Neural Network , U2R attack
تاريخ النشر: 2010
Abstract: Intrusion detection using artificial neural networks is an ongoing area and thus interest in this field has increased among the researchers. Therefore, in this paper we present a system for tackling User to Root (U2R) attacks using generalized feedforward neural network. A backpropagation algorithm is used for training and testing purpose. The system uses sampled data from Kddcup99 dataset, an attack database that is a standard for evaluating the security detection mechanisms. The system is implemented in two phases such as training phase and testing phase. The developed system is applied to different U2R attacks to test its performance. Furthermore, the results indicate that this approach is more precise and accurate in case of false positive, false negative and detection rate.
URI: http://hdl.handle.net/123456789/15711
يظهر في المجموعات:College of Computer and Information Sciences

:الملفات في هذا العنصر

ملف وصف حجمالنوع
Lct.Iftikhar_Conf_4.docx15.22 kBMicrosoft Word XMLعرض\u0641تح

جميع جميع الابحاث محمية بموجب حقوق الطباعة، جميع الحقوق محفوظة.

 

البرمجيات DSpace حقوق المؤلف © 2002-2009 معهد ماساتشوستس للتكنولوجيا و Hewlet Packard - التغذية الراجعة