Due to the promising market prospects, more and more enterprises invest cloud storage and offer on-demand data storage services, which are characterized by distinct quality; thus, users would dynamically change the cloud service providers and transfer their outsourced data. However, the original cloud server might not honestly transfer the outsourced data for saving overhead, and the outsourced data might be polluted during the transfer process. Therefore, how to achieve secure outsourced data transfer has become a primary concern of users. To solve this issue, we put forward a solution for the issue of provable cloud data transfer, which can also simultaneously realize efficient data integrity auditing. By taking the advantages of Merkle sum hash tree (MSHT), our presented scheme can satisfy verifiability without dependency on a third party auditor (TPA). Meanwhile, the formal security proof can demonstrate that our presented scheme meets all of the expected security requirements. Finally, we implement our presented scheme and offer the precise performance evaluation, which can intuitively show the high-efficiency and practicality of our presented scheme in the real-world applications.

With the rapid development of information technology, the internet of things (IoT) technology has been integrated into most people’s daily life and work. However, the IoT must confront many new security challenges. Specifically, the increase in the variety of IoT-connected devices has diversified the network. Meanwhile, the high data rates and spectral efficiency offered by 5G cellular networks facilitates the increasing capacity of IoT network traffic. Therefore, network traffic data are characterized by an expanded large scale, wide diversity, and high dimensions, which greatly affects the functionality and efficiency of intrusion detection methods. Although the existing neural network-based intrusion detection methods partially resolve the above problems, they need to execute a lot of nonlinear transformations when learning and characterizing data, resulting in a large loss of feature information. To address this problem, in this paper, we first design a new neural network model based on the gate recurrent unit (GRU), namely, the supplement gate recurrent unit (SGRU). Compared with a traditional GRU, through loss compensation, a SGRU can reduce the loss of feature information caused by nonlinear transformations when learning and characterizing network traffic data. Then, we adopt the SGRU to propose a novel intrusion detection method to monitor the security of the network. Finally, we developed the corresponding prototype system and verified its performance. The experimental results demonstrate that our proposed intrusion detection method is more accurate than previous intrusion detection methods.