ABSTRACT The Internet of Things is growing tremendously due to new technologies, advancements, and big data. With the digitization of data and continuous technological progress, network data traffic has seen a significant increase. This growth makes IoT networks more vulnerable to attacks because of the rising number of devices and the massive amount of data they generate. One of the emerging topics in the research field is security in IoT. The enormous volume of data poses significant challenges to privacy and cybersecurity, and the frequency of attacks is directly proportional to Internet usage. Intrusion Detection Systems (IDS) have proven effective in detecting various attacks, malicious activities, and unauthorized access in IoT networks, helping to prevent intrusions. Furthermore, advanced AI technologies such as machine learning, deep learning, ensemble learning, and transfer learning have shown promising results in efficiently identifying intrusions, attacks, and malicious actions. This paper presents the development of an effective Intrusion Detection System using Machine and Deep Learning algorithms, compares their performance, and identifies the most effective algorithm for securing IoT data while preserving privacy. Random Forest, Convolutional Neural Networks, and Deep Neural Networks are implemented, tested, and compared with other machine learning algorithms, including Decision Trees, Gaussian Naïve Bayes, and XG‐Boost. The implementation is carried out in Python, using the benchmark KDD dataset. This paper covers the processes of data generation, preprocessing, analysis, and intrusion detection. The experimental results are compared with other state‐of‐the‐art methods to evaluate overall performance. The performance metrics such as accuracy, precision, recall, and F1 score have been computed for the case of deep learning and machine learning for given IoT network.
Abdullah Saleh Alqahtani (Thu,) studied this question.