Anomaly-based intrusion detection system in IoT using kernel extreme learning machine

The Internet of Things (IoT) has developed rapidly and been integrated with a variety of domains. Such a technology allows devices to send, receive, and process data without human involvement. Even though IoT has been widely adopted in several critical domains because it facilitates human life and improves quality of service, its security and privacy issues remain a major challenge. As a relief, an anomaly-based Intrusion Detection System (IDS) can be deployed as a security function to safeguard IoT networks from a diverse range of cyber-attacks. In this paper, an anomaly-based IDS is proposed to overcome a diverse range of cyber-attacks in IoT environments. The proposed method uses the kernel principal component analysis technique to minimize the dimension of data features and to improve the anomaly detection performance. We employ the kernel extreme learning machine to determine whether the traffic flow is benign or malicious for binary classification, and to classify the group of attacks to its specific type for multiclass classification. To validate the efficacy of the proposed anomaly detection method, two modern datasets are used to evaluate and analyze the performance results. The evaluation results demonstrate that the proposed anomaly detection approach can effectively improve the detection efficiency and significantly enhance the detection performance results in terms of accuracy rate, specificity rate, sensitivity rate, F1-score, and the area under curve.