An Ensemble Edge Computing Approach for SD-IoT security Using Ensemble of Feature Selection Methods and Classification

Both academics and the IT industry are now researching the Internet of Things and software-defined networks. They have received a number of criticisms in the SD-IoT due to their novelty. One of the 5 G technologies that makes it possible to construct complex, controllable, economical, and adaptive networks is software-defined networking (SDN). In contrast, edge computing (EC) uses data from sensors, network switches, or other devices to automatically do analytical computing rather than waiting for the data to be sent back to a centralised data repository. This article offers a study on feature selection using an ensemble of filter methods to create a lightweight IDS for SD-IoT edge devices that support OpenFlow in order to defend against such attacks. To create the ensemble of filter methods, three filter-based methods, namely Pearson’s correlation coefficient (PCC), mutual information (MI), and Fisher’s score, have been used. The features selected by this ensemble is sent to the ensemble of classifiers called stack of the classifiers that comprises of support vector machine (SVM) and K-nearest neighbour (KNN) at level ’0’ and logistic regression (LR) at level ’1’. To check the effectiveness of the selected features, stack of the classifiers and individual classifiers are trained and tested with ’All’ and ’Selected’ features, and then their performances are compared. Two datasets, the BoT-IoT dataset and the TON-IoT dataset, were utilised to complete this work. The performance is compared under some performance measuring metrics, namely recall, accuracy, FAR, F1, precision, CKC, and prediction time. It has been discovered that classifiers perform better when trained with selected features rather than all the features. Also, it is discovered that stack of the classifiers with chosen features outperforms all individual classifiers, hence it is chosen for deployment in OpenFlow enabled edge devices of the SD-IoT data plane where it can identify and counteract threats in real-world settings. This offers the SD-IoT distributed attack detection approach.