Designing accurate lightweight intrusion detection systems for IoT networks using fine-tuned linear SVM and feature selectors

Abstract

Intrusion detection systems (IDSs) play a crucial role in ensuring the security and integrity of Internet of Things (IoT) networks by blocking unwanted packets and facilitating secure traffic flow. However, traditional IDSs based on data mining, fuzzy logic, heuristics, rough sets, or conventional machine learning (ML) techniques often lack accuracy and are not energy efficient, primarily due to inappropriate feature selection or the use of all features in datasets. To address these challenges, this study proposes a lightweight, accurate, and high-performance IDSs for IoT networks using fine-tuned Linear Support Vector Machines (LSVMs) and feature selection methods. Four feature selectors, including Importance Coefficient-, Forward- and Backward-Sequential-, and Correlation Coefficient-based approaches, were applied to identify the most important and efficient features from three datasets: KDD Cup-1999, BotIoT-2018, and N-BaIoT-2021. The fine-tuned LSVMs algorithm was then trained on subsets of the selected and full features of the datasets to detect various IoT botnet attacks. Evaluation results show that the IDS models trained with subsets of relevant features outperform those trained with the full feature sets of the datasets in terms of training and test performance and accuracy. The study concludes that it is possible to develop lightweight IDSs by training them with a reduced number of features (6) instead of using the full features (40, 15, 115) in KDD Cup-1999, BotIoT-2018, and N-BaIoT-2021, respectively. The findings highlight a potential for significantly improving the efficiency and accuracy of IDSs on IoT networks using the fine-tuned feature selectors and LSVMs.