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.
作者：
Chen, JunchaoZhou, Jian-taoHao, XinyuInner Mongolia Univ
Natl & Local Joint Engn Res Ctr Intelligent Infor Inner Mongolia Engn Lab Cloud Comp & Serv Softwar Inner Mongolia Key Lab Social Comp & Data ProcCo Hohhot Peoples R China
The accuracy of data analysis depends on data quality, and addressing data consistency issues is a key challenge to improve it. Constant Conditional Functional Dependency (CCFD) is an effective approach that ensures d...
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ISBN:
(纸本)9798350376975;9798350376968
The accuracy of data analysis depends on data quality, and addressing data consistency issues is a key challenge to improve it. Constant Conditional Functional Dependency (CCFD) is an effective approach that ensures data consistency by enforcing bindings of semantically related values, thus providing quality assurance for data analysis and decision-making processes. However, with the growth of data scale, especially the increasing number of data tuples and attributes, existing single-machine CCFD discovery algorithms face issues of low computational efficiency and lengthy computation time. This paper proposes a time-efficient distributed CCFD discovery algorithm (DCCFD). Through the optimization of data preprocessing and index mapping, the data organization structure is enhanced, laying the foundation for the discovery of CCFDs under distributed conditions. The Spark parallel computing framework is used to partition the dataset, which accelerates the parallel loading and processing of data. Additionally, this algorithm ensures accuracy and processing speed when discovering dependencies by efficiently generating frequent itemsets and verifying CCFDs in parallel. Experiments on multiple real datasets show that, especially with the complex Airline dataset, the DCCFD algorithm not only accurately discovers CCFDs, but also reduces the average running time by 75.64% compared with the preCFDMiner algorithm.
Surrogate-assisted evolutionary algorithms (SAEAs) have become a popular method to solve data-driven optimization problems (DOPs), which are common in industry. However, with the development of the Internet of Things,...
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In this paper we propose P4-based Atomic Multicast (P4mCast), a new in-network atomic multicast protocol to support total order guarantees for State Machine Replication (SMR) in cloud-based fault-tolerant and distribu...
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ISBN:
(纸本)9798350351712;9798350351729
In this paper we propose P4-based Atomic Multicast (P4mCast), a new in-network atomic multicast protocol to support total order guarantees for State Machine Replication (SMR) in cloud-based fault-tolerant and distributed applications. P4mCast builds on in-network computing, applying leader-based consensus to groups of prominent P4 programmable switches in modern data center networks. P4mCast achieves significantly lower latency overhead in the microseconds scale while increasing the throughput one order of magnitude higher compared to state of the art software-based solutions.
Continuous monitoring is a major component of many applications in wireless sensor network (WSN). In these applications, to reduce the communication overhead, some form of data summary or aggregation can be performed....
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ISBN:
(纸本)9781665495127
Continuous monitoring is a major component of many applications in wireless sensor network (WSN). In these applications, to reduce the communication overhead, some form of data summary or aggregation can be performed. However, performing non-trivial in-network data processing such as finding frequent items, Top-K monitoring, and clustering efficiently are challenging in practice. In this paper, we present Low-Power Distinct Sum (LDS), a distributed in-network data aggregation primitive that performs the sum of unique items in WSN. LDS serves as the underlying primitive that can be used to implement many distributed data processing efficiently. To demonstrate LDS's capabilities, we design and implement a distributed data streaming application with LDS running on Contiki OS. Compared to the baseline algorithm, LDS can reduce the completion time by up to 66%.
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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The proceedings contain 37 papers. The topics discussed include: few-shot image classification method with label consistent and inconsistent self-supervised learning;a high performance ai-powered cache mechanism for I...
ISBN:
(纸本)9798331506896
The proceedings contain 37 papers. The topics discussed include: few-shot image classification method with label consistent and inconsistent self-supervised learning;a high performance ai-powered cache mechanism for IoT devices;Q-MMT: Qinqiang Opera generation based on multi-track music transformer;research on human combination continuous motion recognition method;multispectral registration algorithm based on fusion of infrared and color images;improved detection of forged and generated facial images based on ResNet-50;intelligent spectrum management for UAV swarm with multiple combat missions: a DQN-based solution;multispectral registration algorithm based on fusion of infrared and color images;and OCDE: adaptive differential evolution algorithm in distributed intelligent systems.
The Internet of Bio-Nano Things (IoBNT) is an innovative field of research located at the intersection of nanotechnology, biotechnology and information and communication technologies. It aims to enable the seamless in...
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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.
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.
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