The Internet of things (IoT) has increased rapidly which led to a huge number of IOT cyberattacks. Malicious operations, MITM, DoS/DDoS, scan, and Botnet attacks are the primary attacks that can affect IoT devices security. Therefore, an accurate and efficient system that identifies malicious attacks and helps in reducing security risks associated to IoT devices has become mandatory. Network intrusion detection system has been generally admitted as an efficient and effective approach for dealing with network security threats. Also, to address increasingly sophisticated cyber security threats, machine learning methods in established IDS are used through an efficient and effective detection process. In this work, various machine learning classifiers, including K-Nearest Neighbors (KNN), Support Vector Machines (SVM), Random Forests (RF), Decision Trees (DT), Gaussian Naïve Bays (NB), XGBoost (XGB), Logistic Regression (LR), and Artificial Neural Networks: Multilayer Perceptron (MLP) is used to identify various categories of network attacks on IoT devices and to determine the best algorithm that leads to a good performance in detecting patterns of suspect IoT network activities. The IoT Botnet dataset has assessed such models using different performance measures such as precision, accuracy, recall, and F1-score. As a result, we observe that the studied models reached the best accuracy about 99.60% by implementing the MLP algorithm for multiclass traffic detection. Moreover, it shows a good performance concerning training time and prediction execution time, less than 307 ms.

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