Experimental analysis of classification for different Internet of Things (IoT) network attacks using machine learning and deep learning

Abstract

The internet of things is one of today's most revolutionary technologies. Because of its pervasiveness, increasing network connection capacity, and diversity of linked items, the internet of things (IoT) is adaptable and versatile. The most common problem impeding IoT growth is insufficient security measures. The threat of data breaches is always there since smart gadgets gather and transmit sensitive information that, if disclosed, might have severe consequences. In this article, to identify and classify IoT network attacks, we have analyzed six machine learning and deep learning approaches: Decision Tree, Random Forest, AdaBoost, XGBoost, ANN and MLP. Accuracy, Precision, Recall, F1-Score, Confusion Matrix are some of the metrics we have used to evaluate our models. We have achieved fairly impressive results (above 96%) in binary classification for all the techniques. When all of the classifiers were analyzed, Decision Tree and Random Forest outperformed all others (above 99%) for both binary and multiclass classification. Adaboost and ANN, on the other hand, perform badly for multiclass classification. We have also applied Undersampling, Oversampling and SMOTE techniques on a dataset to reduce data skewness and to evaluate multiple ML and DL algorithms. The feasibility of the techniques suggested in this work is demonstrated on the IoT/IIoT dataset of TON_IoT datasets, which incorporate data obtained from Telemetry datasets of IoT and IIoT sensors.