Enhancing IoT Network Security Using Feature Selection for Intrusion Detection Systems

The Internet of Things (IoT) connects people, devices, and processes in multiple ways, resulting in the rapid transformation of several industries. Apart from several positive impacts, the IoT presents various challenges that must be overcome. Considering that related devices are often resource-constrained and are deployed in insecure environments, the proliferation of IoT devices causes several security concerns. Given these vulnerabilities, this paper presents criteria for identifying those features most closely related to such vulnerabilities to help enhance anomaly-based intrusion detection systems (IDSs). This study uses the RT-IoT2022 dataset, sourced from the UCI Machine Learning Repository, which was specifically developed for real-time IoT intrusion detection tasks. Feature selection is performed by combining the concepts of information gain, gain ratio, correlation-based feature selection, Pearson’s correlation analysis, and symmetric uncertainty. This approach offers new insights into the tasks of detecting and mitigating IoT-based threats by analyzing the major correlations between several features of the network and specific types of attacks, such as the relationship between ‘fwd_init_window_size’ and SYN flood attacks. The proposed IDS framework is an accurate framework that can be integrated with real-time applications and provides a robust solution to IoT security threats. These selected features can be applied to machine learning and deep learning classifiers to further enhance detection capabilities in IoT environments.