GSFI_SMOTE: a hybrid multiclass classifier for minority attack detection in internet of things network

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.