Zero Trust security has recently gained attention in enterprise network security. One of its key ideas is making network-level access decisions based on trust scores. However, score-based access control in the enterpr...
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ISBN:
(纸本)9798350381993;9798350382006
Zero Trust security has recently gained attention in enterprise network security. One of its key ideas is making network-level access decisions based on trust scores. However, score-based access control in the enterprise domain still lacks essential elements in our understanding, and in this paper, we contribute with respect to three crucial aspects. First, we provide a comprehensive list of 29 trust attributes that can be used to calculate a trust score. By introducing a novel mathematical approach, we demonstrate how to quantify these attributes. Second, we describe a dynamic risk-based method to calculate the trust threshold the trust score must meet for permitted access. Third, we introduce a novel trust algorithm based on Subjective Logic that incorporates the first two contributions and offers fine-grained decision possibilities. We discuss how this algorithm shows a higher expressiveness compared to a lightweight additive trust algorithm. Performance-wise, a prototype of the Subjective Logic-based approach showed similar calculation times for making an access decision as the additive approach. In addition, the dynamic threshold calculation showed only 7% increased decision-making times compared to a static threshold.
The Industrial Internet of Things (IIoT) generally uses cloud computing mode to process tasks. However, in cases of excessive task volume, a high workload will lead to significant processing delays. Edge computing, du...
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Fault-tolerant distributed algorithms such as Reliable Broadcast, Causal Broadcast, Total Order Broadcast, and Consensus, are at the core of many modern distributedsystems. However, the development of distributed alg...
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ISBN:
(纸本)9798350325454
Fault-tolerant distributed algorithms such as Reliable Broadcast, Causal Broadcast, Total Order Broadcast, and Consensus, are at the core of many modern distributedsystems. However, the development of distributed algorithms by humans is a laborious and complex process. This work presents a novel approach to generating distributed algorithms using Generative Artificial Intelligence that allows for automating the process of generating such algorithms. The paper also summarizes our initial results on using the approach to generate Reliable Broadcast algorithms.
In this age of digital computing, security is very essential. When building and implementing systems, security in distributedsystems presents special issues that must be taken into account. There are various security...
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Clustered storage systems often deploy erasure coding that encodes data into coded chunks and distributes them across nodes to tolerate node failures. It is a storage-efficient redundancy scheme but incurs high repair...
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ISBN:
(纸本)9798350307924
Clustered storage systems often deploy erasure coding that encodes data into coded chunks and distributes them across nodes to tolerate node failures. It is a storage-efficient redundancy scheme but incurs high repair penalty;thus some state-of-the-arts aim to pipeline the above repair process to improve the repair performance. However, we observe that all existing repair pipelining methods only use a single pipeline, making network bandwidth resources of storage nodes underutilized. In this paper, we propose FullRepair, a new repair pipelining mechanism based on multiple pipelines with the aim of fully exploiting all available bandwidth resources during repair. We construct four constraints to model the repair pipelining problem such that we can obtain the optimal pipelined repair throughput under full bandwidth utilization. We design a multi-pipeline scheduling scheme for FullRepair so as to achieve the above optimality. Experiments on the Amazon EC2 show that compared with the state-of-the-art repair pipelining methods RP and PivotRepair, FullRepair reduces the repair time of single chunk by up to 45.40% and 33.19%, respectively.
This paper presents a secure and flexible process integration approach enabling distributed data fusion in military IoT applications. It seamlessly combines two recently developed technologies, the Dynamic Process Int...
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Satellite-based Remote Area Observation systems are becoming increasingly popular in the upcoming 6G world. However, traditional Earth Observation (EO) systems suffer from communication requirements, reliability, and ...
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ISBN:
(纸本)9798350309492;9798350309485
Satellite-based Remote Area Observation systems are becoming increasingly popular in the upcoming 6G world. However, traditional Earth Observation (EO) systems suffer from communication requirements, reliability, and data privacy issues. To address these issues, we propose a multilayered Non-Terrestrial Network (NTN) based EO framework for remote area observation purposes. The proposed framework includes the air network along with traditional satellite networks for reliable and low-cost EO services. Additionally, with onboard edge computing facilities, the proposed EO framework can process data in space. Next, given the importance of intelligent services in the 6G world, we extend the multi-layered EO framework and propose a novel distributed Learning (DL) solution for federated training. The proposed framework is defined as Generalized Federated Split Transfer Learning (GFSTL), which can induce split and transfer learning tools into a federated learning framework for improving overall training performance and accuracy. Moreover, GFSTL uses Unmanned Aerial Vehicles (UAVs) for improved data accuracy and image quality in challenging terrains, ensuring increased accuracy in EO applications, and establishes a resilient model for efficient and secure training across distributed platforms, making it both efficient and accurate. In addition, SL helps resource-constrained UAVs perform the task efficiently, enhancing scalability and extensibility. Finally, we conduct experiments to provide theoretical and numerical insight into the performance of the proposed method.
Attracting research interests and applications in academic and industrial community due to the proliferation of mobile devices, computation and processing services on spatio-temporal trajectory data has usually been o...
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ISBN:
(纸本)9798350381993;9798350382006
Attracting research interests and applications in academic and industrial community due to the proliferation of mobile devices, computation and processing services on spatio-temporal trajectory data has usually been outsourced to cloud platforms to save the costs of data storage, computation and management. To prevent the privacy leakage of trajectories from the direct data outsourcing, in this paper, we study the secure similarity search problem on spatio-temporal trajectories and present a secure synchronized spatio-temporal trajectory similarity search approach. In the approach, adopting the Matching Point-point distance Similarity (MPS) measurement, we first propose a Secure Matching Point-point Distance Similarity Computation (SMPSC) Protocol to support the secure similarity calculation on encrypted trajectories. To improve the computational performance, we further propose a Secure Grid Filtering (SGF) method by matching spatio-temporal grid codes to filter the dissimilar trajectories based on the distance threshold in MPS. At last, with SMPSC protocol and SGF method, we propose a Secure Synchronized Spatio-Temporal Trajectory similarity Search Processing (S-3 TS) method to retrieve similar trajectories based on MPS measurement. We theoretically analyze the computational complexity and security guarantees of the presented approach, and conduct extensive experiments on real and synthetic datasets to demonstrate its search performance.
The enhancement of the transmission range in orbital angular momentum (OAM) multiplexing systems is anticipated to benefit significantly from the implementation of a distributed receiving strategy. This approach entai...
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ISBN:
(纸本)9798350304060;9798350304053
The enhancement of the transmission range in orbital angular momentum (OAM) multiplexing systems is anticipated to benefit significantly from the implementation of a distributed receiving strategy. This approach entails the strategic placement of multiple receiving antennas in a discrete and independent manner across a spiral phase plane, which is oriented perpendicularly to the direction of transmission. Given the lack of synchronization among the receiving antennas, a prerequisite for the de-multiplexing process is the execution of phase compensation on all incoming signals. To mitigate the expenses associated with Radio Frequency (RF) processing, this study introduces an innovative Intermediate Frequency (IF) phase compensation algorithm, accompanied by a uniquely structured data frame. By aligning the phases of signals received by the distributed antennas at the intermediate frequency level, the process facilitates the subsequent de-multiplexing to segregate each OAM channel. Through both simulation and empirical experimentation, it has been demonstrated that this methodology substantially diminishes the crosstalk prevalent among multiplexed OAM channels, thereby promoting a consistent and stable transmission rate within the system. This advancement underscores the potential of distributed receiving schemes in enhancing the efficacy and reliability of OAM multiplexing systems.
Stream processing has become a critical component in the architecture of modern applications. With the exponential growth of data generation from sources such as the Internet of Things, business intelligence, and tele...
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ISBN:
(纸本)9798350343946
Stream processing has become a critical component in the architecture of modern applications. With the exponential growth of data generation from sources such as the Internet of Things, business intelligence, and telecommunications, real-time processing of unbounded data streams has become a necessity. DSP systems provide a solution to this challenge, offering high horizontal scalability, fault-tolerant execution, and the ability to process data streams from multiple sources in a single DSP job. Often enough though, data streams need to be enriched with extra information for correct processing, which introduces additional dependencies and potential bottlenecks. In this paper, we present an in-depth evaluation of data enrichment methods for DSP systems and identify the different use cases for stream processing in modern systems. Using a representative DSP system and conducting the evaluation in a realistic cloud environment, we found that outsourcing enrichment data to the DSP system can improve performance for specific use cases. However, this increased resource consumption highlights the need for stream processing solutions specifically designed for the performance-intensive workloads of cloud-based applications.
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