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Intrusion Detection Systems (IDSs) are crucial for safeguarding modern IoT communication networks against cyberattacks. IDSs must exhibit exceptional performance, low false positive rates, and significant flexibility in constructing attack patterns to efficiently identify and neutralize these attacks. This research paper discusses the use of an Extreme Learning Machine (ELM) as a new technique to enhance the performance of IDSs. The study utilizes two standard IDS-based IoT network datasets: NSL-KDD 2009 via Distilled-Kitsune 2021. Both datasets are used to assess the effectiveness of ELM in a conventional supervised learning setting. The study investigates the capacity of the ELM algorithm to handle high-dimensional and unbalanced data, indicating the potential to enhance IDS accuracy and efficiency. The research also examines the setup of ELM for both NSL_KDD and Kitsune using Python and Google COLAB to do binary and multi-class classification. The experimental evaluation revealed the proficient performance of the proposed ELM-based IDS among other implemented supervised learning-based IDSs and other state-of-the-art models in the same study area. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) devices are widely used but also vulnerable to cyberattacks that can cause security issues. To protect against this, machine learning approaches have been developed for network intrusion detection in IoT. These often use feature reduction techniques like feature selection or extraction before feeding data to models. This helps make detection efficient for real-time needs. This paper thoroughly compares feature extraction and selection for IoT network intrusion detection in machine learning-based attack classification framework. It looks at performance metrics like accuracy, f1-score, and runtime, etc. on the heterogenous IoT dataset named Network TON-IoT using binary and multiclass classification. Overall, feature extraction gives better detection performance than feature selection as the number of features is small. Moreover, extraction shows less feature reduction compared with that of selection, and is less sensitive to changes in the number of features. However, feature selection achieves less model training and inference time compared with its counterpart. Also, more space to improve the accuracy for selection than extraction when the number of features changes. This holds for both binary and multiclass classification. The study provides guidelines for selecting appropriate intrusion detection methods for particular scenarios. Before, the TON-IoT heterogeneous IoT dataset comparison and recommendations were overlooked. Overall, the research presents a thorough comparison of feature reduction techniques for machine learning-driven intrusion detection in IoT networks. [ABSTRACT FROM AUTHOR]