eprintid: 1015
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/10/15
datestamp: 2023-11-09 15:49:10
lastmod: 2023-11-09 15:49:10
status_changed: 2023-11-09 15:38:52
type: article
metadata_visibility: show
creators_name: Ahmad, I.
creators_name: Abdullah, A.
creators_name: Alghamdi, A.
title: Investigating supervised neural networks to intrusion detection
ispublished: pub
keywords: Detection mechanism; Intrusion detection approaches; Intrusion detection system (IDS); Intrusion Detection Systems; Mean squared error; Multi-criteria analysis; Supervised neural networks, Computer crime; Error detection; Mean square error; Neural networks, Intrusion detection
note: cited By 2
abstract: The application of neural networks towards intrusion detection is becoming a mainstream and a useful approach to deal with several current issues in this area. Currently, security in computer and network is a main problem because a single intrusion may cause a very big harm. A variety of neural networks is applied to intrusion detection approaches during last few years and still is being used in this area. In this paper, we investigated different supervised neural networks (SNN) to intrusion detection. This work describes an analysis of different supervised neural network applied to intrusion detection mechanisms using Multi-criteria analysis (MCA) technique. Further, conclusion on results is made and direction for future works is presented. The outcome of this effort may assist and direct the security implementers in the area of intrusion detection systems or approaches. ICIC International © 2010 ISSN 1881-803X.
date: 2010
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-78650266320&partnerID=40&md5=a92a64c3bc0b8c92c148984509036bf2
full_text_status: none
publication: ICIC Express Letters
volume: 4
number: 6 A
pagerange: 2133-2138
refereed: TRUE
issn: 1881803X
citation:   Ahmad, I. and Abdullah, A. and Alghamdi, A.  (2010) Investigating supervised neural networks to intrusion detection.  ICIC Express Letters, 4 (6 A).  pp. 2133-2138.  ISSN 1881803X