An anonymous authentication and secure data transmission scheme for the Internet of Things based on blockchain

With the widespread use of network infrastructures such as 5G and low-power wide-area networks,a large number of the Internet of Things(IoT)device nodes are connected to the network,generating massive amounts of data.Therefore,it is a great challenge to achieve anonymous authentication of IoT nodes and secure data transmission.At present,blockchain technology is widely used in authentication and s data storage due to its decentralization and immutability.Recently,Fan et al.proposed a secure and efficient blockchain-based IoT authentication and data sharing scheme.We studied it as one of the state-of-the-art protocols and found that this scheme does not consider the resistance to ephemeral secret compromise attacks and the anonymity of IoT nodes.To overcome these security flaws,this paper proposes an enhanced authentication and data transmission scheme,which is verified by formal security proofs and informal security analysis.Furthermore,Scyther is applied to prove the security of the proposed scheme.Moreover,it is demonstrated that the proposed scheme achieves better performance in terms of communication and computational cost compared to other related schemes.

An accurate identification method for network devices based on spatial attention mechanism

With the metaverse being the development direction of the next generation Internet,the popularity of intelligent devices,and the maturity of various emerging technologies,more and more intelligent devices try to connect to the Internet,which poses a major threat to the management and security protection of network equipment.At present,the mainstream method of network equipment identification in the metaverse is to obtain the network traffic data generated in the process of device communication,extract the device features through analysis and processing,and identify the device based on a variety of learning algorithms.Such methods often require manual participation,and it is difficult to capture the small differences between similar devices,leading to identification errors.Therefore,we propose a deep learning device recognition method based on a spatial attention mechanism.Firstly,we extract the required feature fields from the acquired network traffic data.Then,we normalize the data and convert it into grayscale images.After that,we add a spatial attention mechanism to CNN and MLP respectively to increase the difference between similar network devices and further improve the recognition accuracy.Finally,we identify devices based on the deep learning model.A large number of experiments were carried out on 31 types of network devices such as web cameras,wireless routers,and smartwatches.The results show that the accuracy of the proposed recognition method based on the spatial attention mechanism is increased by 0.8%and 2.0%,respectively,compared with the recognition method based only on the deep learning model under the CNN and MLP models.The method proposed in this paper is significantly superior to the existing method of device-type recognition based only on a deep learning model.