Single point of failure (SPOF) attacks and privacy infringement problems are contributing to the increasing number of security incidents in central security systems currently being used in industrial Internet of Things (IIoT) environments. In this stu...
Single point of failure (SPOF) attacks and privacy infringement problems are contributing to the increasing number of security incidents in central security systems currently being used in industrial Internet of Things (IIoT) environments. In this study, we provide a decentralized security framework based on edge intelligence that can minimize sophisticated cyber threats that can occur in IoT situations with limited resources. The proposed framework performs intelligent anomaly detection by simultaneously extracting the geographical and temporal characteristics of IoT time series data at the edge end that processes IoT data first. Moreover, it guarantees safety because it connects IoT data with blockchain without directly exposing IoT data at the edge end to improve security privacy. Since the proposed framework uses a consensus algorithm based on the reliability score that measures the security behavior history of IoT nodes, it has increased the security reliability of the network and improved the consensus throughput. As a result of the experiments, the proposed framework has reduced the consensus latency by more than 4.1% on average and improved the detection accuracy by 6.5% on average compared to the current single model.