eprintid: 19127
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/91/27
datestamp: 2024-06-04 14:11:34
lastmod: 2024-06-04 14:11:34
status_changed: 2024-06-04 14:04:56
type: article
metadata_visibility: show
creators_name: Abbas, Q.
creators_name: Hina, S.
creators_name: Sajjad, H.
creators_name: Zaidi, K.S.
creators_name: Akbar, R.
title: Optimization of predictive performance of intrusion detection system using hybrid ensemble model for secure systems
ispublished: pub
keywords: Computer crime; Cost effectiveness; Deep learning; Internet of things; Intrusion detection; Investments; Network security, Ensemble methods; Ensemble models; Intrusion Detection Systems; Intrusion-Detection; Networks and systems; Optimisations; Predictive models; Predictive performance; Secure system; Small-and-medium enterprise, Developing countries
note: cited By 1
abstract: Network intrusion is one of the main threats to organizational networks and systems. Its timely detection is a profound challenge for the security of networks and systems. The situation is even more challenging for small and medium enterprises (SMEs) of developing countries where limited resources and investment in deploying foreign security controls and development of indigenous security solutions are big hurdles. A robust, yet cost-effective network intrusion detection system is required to secure traditional and Internet of Things (IoT) networks to confront such escalating security challenges in SMEs. In the present research, a novel hybrid ensemble model using random forest-recursive feature elimination (RF-RFE) method is proposed to increase the predictive performance of intrusion detection system (IDS). Compared to the deep learning paradigm, the proposed machine learning ensemble method could yield the state-of-the-art results with lower computational cost and less training time. The evaluation of the proposed ensemble machine leaning model shows 99, 98.53 and 99.9 overall accuracy for NSL-KDD, UNSW-NB15 and CSE-CIC-IDS2018 datasets, respectively. The results show that the proposed ensemble method successfully optimizes the performance of intrusion detection systems. The outcome of the research is significant and contributes to the performance efficiency of intrusion detection systems and developing secure systems and applications. © 2023 Abbas et al.
date: 2023
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85172319065&doi=10.7717%2fpeerj-cs.1552&partnerID=40&md5=09ae4b8054dffecc2d35565dd96f19b8
id_number: 10.7717/peerj-cs.1552
full_text_status: none
publication: PeerJ Computer Science
volume: 9
refereed: TRUE
citation:   Abbas, Q. and Hina, S. and Sajjad, H. and Zaidi, K.S. and Akbar, R.  (2023) Optimization of predictive performance of intrusion detection system using hybrid ensemble model for secure systems.  PeerJ Computer Science, 9.