Analysis of Key Technologies of Cloud Computing Security Based on Trust Model
01 Jan 2022-pp 404-412
TL;DR: Wang et al. as discussed by the authors proposed a subjective and objective TM based on the ideal point method for the trust problem in the cloud environment, and verifies the performance of the model through experiments.
Abstract: With the development of cloud computing, cloud computing provides more and more computing services. In today's fiercely competitive environment, the service flexibility and choice provided by this highly scalable technology are becoming more and more attractive to enterprises. However, behind the rapid development of cloud computing, its security and trust issues have become increasingly unignorable. The purpose of this paper is to analyze the key technologies of cloud computing security based on the trust model (TM). This paper first proposes a subjective and objective TM based on the ideal point method for the trust problem in the cloud environment, and verifies the performance of the model through experiments. This paper proposes a decentralized TM based on blockchain, and verifies the effectiveness of the model through experiments. This paper starts from the current problems of cloud computing, and on the basis of understanding the shortcomings of traditional security technologies, proposes a key technology of cloud computing security based on the TM. The solution proposed in this paper has a minimum average time consumption of 0.12 ms in the copy + label generation stage, while the minimum average time consumption of a traditional PDP is 16.3 ms. It is obvious that this solution has great advantages.
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TL;DR: A thorough evaluation of real-time, depth-aware augmented reality networks highlights the ability of such fast networks to generalize well to new environments, a crucial feature required to tackle the extremely varied contexts faced in real applications.
Abstract: Depth perception is paramount to tackle real-world problems, ranging from autonomous driving to consumer applications For the latter, depth estimation from a single image represents the most versatile solution, since a standard camera is available on almost any handheld device Nonetheless, two main issues limit its practical deployment: i) the low reliability when deployed in-the-wild and ii) the demanding resource requirements to achieve real-time performance, often not compatible with such devices Therefore, in this paper, we deeply investigate these issues showing how they are both addressable adopting appropriate network design and training strategies -- also outlining how to map the resulting networks on handheld devices to achieve real-time performance Our thorough evaluation highlights the ability of such fast networks to generalize well to new environments, a crucial feature required to tackle the extremely varied contexts faced in real applications Indeed, to further support this evidence, we report experimental results concerning real-time depth-aware augmented reality and image blurring with smartphones in-the-wild
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TL;DR: Simulation results confirm the robustness and accuracy of the proposed adaptive neuro-fuzzy inference system (ANFIS) brain-inspired trust management model (TMM) in identifying malicious nodes in the communication network.
Abstract: Rapid popularity of Internet of Things (IoT) and cloud computing permits neuroscientists to collect multilevel and multichannel brain data to better understand brain functions, diagnose diseases, and devise treatments. To ensure secure and reliable data communication between end-to-end (E2E) devices supported by current IoT and cloud infrastructure, trust management is needed at the IoT and user ends. This paper introduces a Neuro-Fuzzy based Brain-inspired trust management model (TMM) to secure IoT devices and relay nodes, and to ensure data reliability. The proposed TMM utilizes node behavioral trust and data trust estimated using Adaptive Neuro-Fuzzy Inference System and weighted-additive methods respectively to assess the nodes trustworthiness. In contrast to the existing fuzzy based TMMs, the NS2 simulation results confirm the robustness and accuracy of the proposed TMM in identifying malicious nodes in the communication network. With the growing usage of cloud based IoT frameworks in Neuroscience research, integrating the proposed TMM into the existing infrastructure will assure secure and reliable data communication among the E2E devices.
103 citations
TL;DR: In this article, an adaptive neuro-fuzzy inference system (ANFIS) brain-inspired trust management model (TMM) is proposed to secure IoT devices and relay nodes, and to ensure data reliability.
Abstract: Rapid advancement of Internet of Things (IoT) and cloud computing enables neuroscientists to collect multilevel and multichannel brain data to better understand brain functions, diagnose diseases, and devise treatments. To ensure secure and reliable data communication between end-to-end (E2E) devices supported by current IoT and cloud infrastructures, trust management is needed at the IoT and user ends. This paper introduces an adaptive neuro-fuzzy inference system (ANFIS) brain-inspired trust management model (TMM) to secure IoT devices and relay nodes, and to ensure data reliability. The proposed TMM utilizes both node behavioral trust and data trust, which are estimated using ANFIS, and weighted additive methods respectively, to assess the nodes trustworthiness. In contrast to existing fuzzy based TMMs, simulation results confirm the robustness and accuracy of our proposed TMM in identifying malicious nodes in the communication network. With growing usage of cloud based IoT frameworks in Neuroscience research, integrating the proposed TMM into existing infrastructure will assure secure and reliable data communication among E2E devices.
77 citations
TL;DR: In this article, the authors proposed an alternative approach to creating trust in supply chains with diverse IoT elements, which simplifies data sharing and reduces computational, storage, and latency requirements while increasing the security of the IoT-based supply chain management.
Abstract: Accurate data and strategic business processes are crucial to all parties in a supply chain system. However, the absence of mutual trust can create a barrier to implementation. Several studies have shown that supply chains face challenges arising from a lack of trust with respect to the sharing of data. How well each party trusts the data they receive can have a profound influence on management decisions. Blockchain technology has been widely used to process cryptocurrency transactions. Recently, it has also proved to be effective in creating trust in the Internet of things (IoT) domain. Blockchain technology can facilitate mutual trust between parties who would otherwise have been doubtful of each other's data, allowing for more effective and secure sharing of data. However, if the blockchain is not IoT-optimized, companies can experience significant delays and the need for extensive computational capacity. Moreover, there are still some limitations regarding the consensus between the nodes in the traditional consensus approaches. Here, we propose an alternative approach to creating trust in supply chains with diverse IoT elements. Our streamlined trust model simplifies data sharing and reduces computational, storage, and latency requirements while increasing the security of the IoT-based supply chain management. We evaluate the suggested model using simulations and highlight its viability.
45 citations
TL;DR: It is concluded that eMPC with trust index AP achieved nearly 90% time in the target glucose range and enhanced controller responsiveness to predicted hyper- and hypoglycemia by 26%.
Abstract: Background: We investigated the safety and efficacy of the addition of a trust index to enhanced Model Predictive Control (eMPC) Artificial Pancreas (AP) that works by adjusting the respon...
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TL;DR: In this paper, the authors investigated the limitations of monocular depth estimation on handheld devices and showed how they can be addressed by adopting appropriate network design and training strategies, and also outline how to map the resulting networks to handheld devices to achieve realtime performance.
Abstract: Depth perception is paramount for tackling real-world problems, ranging from autonomous driving to consumer applications. For the latter, depth estimation from a single image would represent the most versatile solution since a standard camera is available on almost any handheld device. Nonetheless, two main issues limit the practical deployment of monocular depth estimation methods on such devices: (i) the low reliability when deployed in the wild and (ii) the resources needed to achieve real-time performance, often not compatible with low-power embedded systems. Therefore, in this paper, we deeply investigate all these issues, showing how they are both addressable by adopting appropriate network design and training strategies. Moreover, we also outline how to map the resulting networks on handheld devices to achieve real-time performance. Our thorough evaluation highlights the ability of such fast networks to generalize well to new environments, a crucial feature required to tackle the extremely varied contexts faced in real applications. Indeed, to further support this evidence, we report experimental results concerning real-time, depth-aware augmented reality and image blurring with smartphones in the wild.
24 citations