Abstract
The Internet of Things (IoT) has been deployed in diverse critical sectors with the aim of improving quality of service and facilitating human lives. The IoT revolution has redefined digital services in different domains by improving efficiency, productivity, and cost-effectiveness. Many service providers have adapted IoT systems or plan to integrate them as integral parts of their systems’ operation; however, IoT security issues remain a significant challenge. To minimize the risk of cyberattacks on IoT networks, anomaly detection based on machine learning can be an effective security solution to overcome a wide range of IoT cyberattacks. Although various detection techniques have been proposed in the literature, existing detection methods address limited cyberattacks and utilize outdated datasets for evaluations. In this paper, we propose an intelligent, effective, and lightweight detection approach to detect several IoT attacks. Our proposed model includes a collaborative feature selection method that selects the best distinctive features and eliminates unnecessary features to build an effective and efficient detection model. In the detection phase, we also proposed an ensemble of learning techniques to improve classification for predicting several different types of IoT attacks. The experimental results show that our proposed method can effectively and efficiently predict several IoT attacks with a higher accuracy rate of 99.984%, a precision rate of 99.982%, a recall rate of 99.984%, and an F1-score of 99.983%.
Highlights
The Internet of Things (IoT) is widely used and has been integrated into a wide range of critical domains, including healthcare, transportation systems, energy, and manufacturing
Each feature selection method nominates best features independently based on their detection performance; features that achieve high scores are added to the optimal feature set, which is utilized in the detection stage
We evaluated our proposed method by comparison with other techniques that used a hybrid deep learning model, proposed by Sahu et al [27]; the accuracy rate of our proposed anomaly detection improved by 0.02%, while precision and recall were improved by 0.08% and 9.24%, respectively
Summary
The Internet of Things (IoT) is widely used and has been integrated into a wide range of critical domains, including healthcare, transportation systems, energy, and manufacturing. This technology enables multiple connected devices to communicate and exchange data with minimal or no human interaction, offering many great advantages for both service providers and end users. IoT applications have transformed buildings, vehicles, health-care systems, and even entire cities into smart objects. With increasing demand for such a technology, the number of IoT devices is expected to reach 83 billion by 2024 [1].
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