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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 …