The development of artificial intelligence and worldwide epidemic events has promoted the implementation of smart healthcare while bringing issues of data privacy, malicious attack, and service quality. The Medical Internet of Things (MIoT), along with the technologies of federated learning and blockchain, has become a feasible solution for these issues. In this paper, we present a blockchain‐based federated learning method for smart healthcare in which the edge nodes maintain the blockchain to resist a single point of failure and MIoT devices implement the federated learning to make full of the distributed clinical data. In particular, we design an adaptive differential privacy algorithm to protect data privacy and gradient verification‐based consensus protocol to detect poisoning attacks. We compare our method with two similar methods on a real‐world diabetes dataset. Promising experimental results show that our method can achieve high model accuracy in acceptable running time while also showing good performance in reducing the privacy budget consumption and resisting poisoning attacks.

Although a cyber-physical system (CPS) enhances the system control flexibility by connecting physical devices to the network, it also increases the possibility of network attacks on the system, which can cause damage to both property and personnel. To achieve CPS security, this paper proposes a network security protection method based on a digital twin (DT). By constructing DT models of the CPS physical layer and network layer and collecting real-time data of the system, the system security is improved from several aspects. First, we construct a data-driven behavior model for the CPS physical layer and introduce expert knowledge in order to realize the function of physical layer anomaly diagnosis. Comparisons with related studies show that our method achieves a higher precision, recall rate, and F1 score. Second, we construct an attack graph model for the CPS network layer for CPS network security analysis in order to realize the functions of security risk quantification and security countermeasure recommendation. Finally, we model the interaction between the physical networks and transfer the diagnosis results of the physical layer twin to the network layer twin in order to correct the attack graph. Thus, we achieve an accurate representation of the overall network security situation in real time.

The increasing number of cyberattacks has made the cybersecurity situation more serious. Thus, it is urgent to use cyber threat intelligence to deal with the complex and changing cyber environment. However, cyber threat intelligence usually exists in an unstructured form, and a huge amount of data poses a great challenge to security analysts. To this end, this paper proposes a novel threat intelligence information extraction system combining multiple models, which contains four key steps: entity extraction, coreference resolution, relation extraction, and knowledge graph construction. In the entity extraction task, a multihead self-attention mechanism is adopted to extract the dependency relationships between words. In the coreference resolution task, contextual information and mention embedding are fused to improve the mention representation. Meanwhile, features of different dimensions are extracted using a convolutional neural network. In the relation extraction task, additional features such as part of speech, mention width, entity type, and distance of entity pairs are incorporated to improve the embedding representation. Finally, a knowledge graph is constructed to explicitly present entities and their relationships. Experimental results indicate that compared with the baseline model, the F1 score of our model is improved by at least 8.87, 9.82, and 10.56 on entity extraction, coreference resolution, and relation extraction, respectively. The knowledge graph in Neo4j demonstrates the effectiveness of our system.