eprintid: 12113
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/21/13
datestamp: 2023-11-10 03:26:39
lastmod: 2023-11-10 03:26:39
status_changed: 2023-11-10 01:16:54
type: article
metadata_visibility: show
creators_name: Parveen Sultana, H.
creators_name: Shrivastava, N.
creators_name: Dominic, D.D.
creators_name: Nalini, N.
creators_name: Balajee, J.M.
title: Comparison of machine learning algorithms to build optimized Network intrusion detection system
ispublished: pub
note: cited By 19
abstract: Network Security is the most important aspect for all products and services offered by networking systems. The network density and usage in information systems, technical systems are humungous and is used by the entire world to provide connectivity from busiest hours to remote locations. Mission critical events, governmental organizations, information technology structures rely on continuous and smooth provision of network connection. This makes the basis of information security pillars-Confidentiality, which means that the data transferred between two users can be readable but should not be understandable, meaning it should be encrypted; Integrity, which focuses on the aspect of reliable message transfer preventing any kind of message tampering in the data transfer process; and finally Authentication and Availability, meaning that the user sending and receiving the data are genuine, and that the data is available, free from denial attacks. Copyright © 2019 American Scientific Publishers All rights reserved.
date: 2019
publisher: American Scientific Publishers
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068583426&doi=10.1166%2fjctn.2019.7929&partnerID=40&md5=1edf861067b60b1889eb8dd97913c6a4
id_number: 10.1166/jctn.2019.7929
full_text_status: none
publication: Journal of Computational and Theoretical Nanoscience
volume: 16
number: 5-6
pagerange: 2541-2549
refereed: TRUE
issn: 15461955
citation:   Parveen Sultana, H. and Shrivastava, N. and Dominic, D.D. and Nalini, N. and Balajee, J.M.  (2019) Comparison of machine learning algorithms to build optimized Network intrusion detection system.  Journal of Computational and Theoretical Nanoscience, 16 (5-6).  pp. 2541-2549.  ISSN 15461955