eprintid: 1207
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/12/07
datestamp: 2023-11-09 15:49:22
lastmod: 2023-11-09 15:49:22
status_changed: 2023-11-09 15:39:13
type: article
metadata_visibility: show
creators_name: Ahmad, I.
creators_name: Abdulah, A.B.
creators_name: Alghamdi, A.S.
title: Towards the designing of a robust intrusion detection system through an optimized advancement of neural networks
ispublished: pub
keywords: Adaptive resonance theory; Adaptive Resonance Theory (ART); Batch Backpropagation (BPROP); Network intrusion detection; Resilient backpropagation; Supervised neural networks; Unsupervised neural networks, Arts computing; Backpropagation; Computer viruses; Conformal mapping; Neural networks; Optimization; Resonance, Intrusion detection
note: cited By 8; Conference of 2nd International Conference on Advanced Science and Technology ; Conference Date: 23 June 2010 Through 25 June 2010; Conference Code:81036
abstract: The duty of securing networks is very difficult due to their size, complexity, diversity and dynamic situation. Currently applying neural networks in intrusion detection is a robust approach to ensure security in the network system. Further, neural networks are alternatives to other approaches in the area of intrusion detection. The main objective of this research is to present an adaptive, flexible and optimize neural network architecture for intrusion detection system that provides the potential to identify network activity in a robust way. The results of this work give directions to enhance security applications such as Intrusion Detection System (IDS), Intrusion Prevention System (IPS), Adaptive Security Alliance (ASA), check points and firewalls and further guide to the security implementers. © 2010 Springer-Verlag.
date: 2010
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-77954579798&doi=10.1007%2f978-3-642-13577-4_53&partnerID=40&md5=0b53a3ee98bc43e9e331d8db452d6626
id_number: 10.1007/978-3-642-13577-4₅₃
full_text_status: none
publication: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
volume: 6059 L
place_of_pub: Miyazaki
pagerange: 597-602
refereed: TRUE
isbn: 3642135765; 9783642135767
issn: 03029743
citation:   Ahmad, I. and Abdulah, A.B. and Alghamdi, A.S.  (2010) Towards the designing of a robust intrusion detection system through an optimized advancement of neural networks.  Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6059 L.  pp. 597-602.  ISSN 03029743