eprintid: 16311
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/63/11
datestamp: 2023-12-19 03:22:51
lastmod: 2023-12-19 03:22:51
status_changed: 2023-12-19 03:06:01
type: article
metadata_visibility: show
creators_name: Adewole, K.S.
creators_name: Salau-Ibrahim, T.T.
creators_name: Imoize, A.L.
creators_name: Oladipo, I.D.
creators_name: AbdulRaheem, M.
creators_name: Awotunde, J.B.
creators_name: Balogun, A.O.
creators_name: Isiaka, R.M.
creators_name: Aro, T.O.
title: Empirical Analysis of Data Streaming and Batch Learning Models for Network Intrusion Detection
ispublished: pub
note: cited By 5
abstract: Network intrusion, such as denial of service, probing attacks, and phishing, comprises some of the complex threats that have put the online community at risk. The increase in the number of these attacks has given rise to a serious interest in the research community to curb the menace. One of the research efforts is to have an intrusion detection mechanism in place. Batch learning and data streaming are approaches used for processing the huge amount of data required for proper intrusion detection. Batch learning, despite its advantages, has been faulted for poor scalability due to the constant re-training of new training instances. Hence, this paper seeks to conduct a comparative study using selected batch learning and data streaming algorithms. The batch learning and data streaming algorithms considered are J48, projective adaptive resonance theory (PART), Hoeffding tree (HT) and OzaBagAdwin (OBA). Furthermore, binary and multiclass classification problems are considered for the tested algorithms. Experimental results show that data streaming algorithms achieved considerably higher performance in binary classification problems when compared with batch learning algorithms. Specifically, binary classification produced J48 (94.73), PART (92.83), HT (98.38), and OBA (99.67), and multiclass classification produced J48 (87.66), PART (87.05), HT (71.98), OBA (82.80) based on accuracy. Hence, the use of data streaming algorithms to solve the scalability issue and allow real-time detection of network intrusion is highly recommended. © 2022 by the authors.
date: 2022
publisher: MDPI
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85139966032&doi=10.3390%2felectronics11193109&partnerID=40&md5=5d5767205cf91ead4e9d196dc0262af3
id_number: 10.3390/electronics11193109
full_text_status: none
publication: Electronics (Switzerland)
volume: 11
number: 19
refereed: TRUE
issn: 20799292
citation:   Adewole, K.S. and Salau-Ibrahim, T.T. and Imoize, A.L. and Oladipo, I.D. and AbdulRaheem, M. and Awotunde, J.B. and Balogun, A.O. and Isiaka, R.M. and Aro, T.O.  (2022) Empirical Analysis of Data Streaming and Batch Learning Models for Network Intrusion Detection.  Electronics (Switzerland), 11 (19).   ISSN 20799292