relation: https://khub.utp.edu.my/scholars/12113/
title: Comparison of machine learning algorithms to build optimized Network intrusion detection system
creator: Parveen Sultana, H.
creator: Shrivastava, N.
creator: Dominic, D.D.
creator: Nalini, N.
creator: Balajee, J.M.
description: 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.
publisher: American Scientific Publishers
date: 2019
type: Article
type: PeerReviewed
identifier:   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     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068583426&doi=10.1166%2fjctn.2019.7929&partnerID=40&md5=1edf861067b60b1889eb8dd97913c6a4
relation: 10.1166/jctn.2019.7929
identifier: 10.1166/jctn.2019.7929