GSFI_SMOTE: a hybrid multiclass classifier for minority attack detection in internet of things network
by Geeta Singh; Neelu Khare
International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC), Vol. 38, No. 1/2/3, 2021

Abstract: The internet of things (IoT) network is prone to several cyber-attacks, due to many obligations of IoT devices. Minority attack detection in the IoT network is a challenge. This paper proposed three multiclassifier models to address this challenge at different IoT layers. Random forest (RF) classifier is the main component in proposed models. RF hyperparameters are tuned with grid-search cross-validation (GSCV) to build an initial GS model that achieves a 100% normal traffic detection rate. It can efficiently separate normal and anomalous traffic at the IoT network layer. GS is extended with a feature importance (FI)-based feature reduction technique and the synthetic minority oversampling technique (SMOTE) successively to realise GSFI and GSFI_SMOTE models that achieve better minority attack detection rates and applicable to the resource-limited fog/edge infrastructure, and the critical IoT infrastructure, respectively. GSFI_SMOTE outperforms the existing methods. The UNSW-NB15 is used as a use case for experimenting with proposed models.

Online publication date: Mon, 22-Nov-2021

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com