Detecting Cyber Threats in IoT Networks: A Machine Learning Approach

Abstract

Internet of Things (IOT) network security challenges in cybersecurity are among the key demands that are oriented towards the safety of data distribution and storage. Prior to the present research, the loopholes that have been found in the field of tackling this danger were the greatest, especially in real-world IoT setups. Hereby, in this study, we create room for the previously unfilled gap using our innovative method to detect network cybersecurity in IoT networks. The technique is based on merging machine learning and neural network algorithms that are trained on vast IoT historical datasets. Several diverse methods, particularly gradient boosting, convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and recurrent neural networks (RNNs), are used to detect and categorize network traffic aspects that potentially suggest cyber risks. The evaluation of each algorithm's performance is based on conventional metrics, which are, for instance, accuracy, precision, recall, and F1-score. Through rigorous testing, we do illustrate the applicability of our technique, in which our solution recognized and curbed th e cyber threat in IoT networks, offering the most accurate results of 93% using gradient boosting. Our discussed work can be taken as confirmation of the current advancement of machine learning and deep learning techniques in the scope of increasing cybersecurity in IoT environments. And furthermore, our examined facts may serve as the starting point of future refined investigations in this regard.