Cloud storage, an economically attractive service offered by cloud service providers (CSPs), has attracted a large number of tenants. However, because the ownership and management of outsourced data are separated, outsourced data faces a lot of security challenges, for instance, data security, data integrity, data update, and so on. In this article, we primarily investigate the problem of efficient data integrity auditing supporting provable data update in cloud computing environment. Subsequently, on the basis of the Merkel sum hash tree (MSHT), we introduce an efficient outsourced data integrity auditing scheme. Our designed scheme could synchronously meet the requirements of provable data update and data confidentiality without dependency on a third authority. At the same time, the numerical analysis shows that the computing complexity logarithmically grows with the number of outsourced subfiles. Finally, a prototype implementation is developed to simulate our designed scheme and measure its performance. The consequences of experiments present that compared with some previous solutions, our designed scheme has much more attractive practicality and higher efficiency in practical 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.
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