eprintid: 995
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/09/95
datestamp: 2023-11-09 15:49:09
lastmod: 2023-11-09 15:49:09
status_changed: 2023-11-09 15:38:50
type: conference_item
metadata_visibility: show
creators_name: Ahmad, I.
creators_name: Abdullah, A.B.
creators_name: Alghamdi, A.S.
title: Remote to local attack detection using supervised neural network
ispublished: pub
keywords: Artificial Neural Network; Attack detection; Data sets; False negative rate; False positive rates; Intrusion detection approaches; Local attack; Supervised neural networks, Feedforward neural networks; Internet, Intrusion detection
note: cited By 6; Conference of 2010 International Conference for Internet Technology and Secured Transactions, ICITST 2010 ; Conference Date: 8 November 2010 Through 11 November 2010; Conference Code:83777
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.
date: 2010
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-79951491256&partnerID=40&md5=fe26cebbffbf045d14588da0c73a6f79
full_text_status: none
publication: 2010 International Conference for Internet Technology and Secured Transactions, ICITST 2010
place_of_pub: London
refereed: TRUE
isbn: 9781424488629
citation:   Ahmad, I. and Abdullah, A.B. and Alghamdi, A.S.  (2010) Remote to local attack detection using supervised neural network.  In: UNSPECIFIED.