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检索条件"任意字段=5th IEEE International Conference on Big Data Intelligence and Computing, DataCom 2019"

共 603 条记录，以下是41-50 订阅

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Parameter Identification of Norton Equivalent Model from Harmonic Monitoring data 5

Parameter Identification of Norton Equivalent Model from Har...

引用

5th ieee international conference on Cloud computing and intelligence Systems (CCIS)

作者： Shao, Zhenguo Xu, Haobo Jiang, Ruobin Fuzhou Univ Coll Elect Engn & Automat Fuzhou Fujian Peoples R China

ISBN: (纸本)9781538660058

A method of identifying the operating parameters of harmonic customers based on historical monitoring data is introduced in this paper. Firstly, PCA is used to reduce the original harmonic data dimensions and determine the appropriate number of clusters. then k -means is used to partition harmonic mode on low dimensions. Lastly group feature parameters are calculated from the clustered typical harmonic operating conditions. Experimental results showed that the operating parameters of harmonic customers can be identified from a large number of high -dimensional historical statistic data by the proposed method, which will also contribute to the harmonic source location and optimal operation strategy.

关键词： PCA typical harmonic operating conditions group feature parameters

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Distributed Active Learning Strategies on Edge computing 6

Distributed Active Learning Strategies on Edge Computing

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6th ieee international conference on Cyber Security and Cloud computing (ieee CSCloud) / 5th ieee international conference on Edge computing and Scalable Cloud (ieee EdgeCom)

作者： Qian, Jia Hansen, Lars Kai Gochhayat, Sarada Prasad Tech Univ Denmark Dept Appl Math & Comp Sci Lyngby Denmark Old Dominion Univ Virginia Modeling Anal & Simulat Ctr Norfolk VA USA

ISBN: (纸本)9781728116617

Fog platform brings the computing power from the remote cloud-side closer to the edge devices to reduce latency, as the unprecedented generation of data causes ineligible latency to process the data in a centralized fashion at the Cloud. In this new setting, edge devices with distributed computing capability, such as sensors, surveillance camera, can communicate with fog nodes with less latency. Furthermore, local computing (at edge side) may improve privacy and trust. In this paper, we present a new method, in which, we decompose the data processing, by dividing them between edge devices and fog nodes, intelligently. We apply active learning on edge devices;and federated learning on the fog node which significantly reduces the data samples to train the model as well as the communication cost. To show the effectiveness of the proposed method, we implemented and evaluated its performance on a benchmark images data set.

关键词： Fog computing Edge computing Active Learning Federated Learning Bayesian Neural Network

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Secure Desktop computing in the Cloud 6

Secure Desktop Computing in the Cloud

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6th ieee international conference on Cyber Security and Cloud computing (ieee CSCloud) / 5th ieee international conference on Edge computing and Scalable Cloud (ieee EdgeCom)

作者： Krisler, Brian Pal, Partha Bertilson, Zech thomsen, Dan Raytheon BBN Techonolgies Cambridge MA 02138 USA Raytheon BBN Technol Cambridge MA USA Raytheon BBN Technol Minneapolis MN USA SIFT LLC Minneapolis MN USA

ISBN: (纸本)9781728116617

As companies begin to transition the computation that employees perform on their desktop and the management of the desktop computing infrastructure to the cloud, the need for securing such cloud-hosted user computing tasks and environments become paramount. In this paper, we present Venia, a secure cloud-based desktop computing platform designed to protect against both external and internal threats. Accessible to end-users through a thin Remote Desktop Protocol (RDP) client Venia isolates end-user's applications and data into containers and subjects the interactions with and among the containers to security policies. Following a principle of least privilege, Venia security policies control user's access to containers, network and file system interaction of the containers, cross-container data sharing and also enables collection of detailed logs for auditing purpose. Venia has been deployed to a 3rd party test environment where it demonstrated that end-users can perform the tasks they need on a daily basis, without introducing greater risk to the overall organization, and its currently undergoing security and performance evaluation by an independent evaluation team.

关键词： cloud computing cyber security end-user computing containerization

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Research on big data Processing Model of Edge-Cloud Collaboration in Cyber-Physical Systems 5

Research on Big Data Processing Model of Edge-Cloud Collabor...

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5th ieee international conference on big data Analytics (ICBDA)

作者： Yue, Zhifeng Zhu, Zhixiang Wang, Chuang Du, Wenbo Xian Univ Posts & Telecommun Inst IoT & IT Based Industrializat Xian Peoples R China

ISBN: (纸本)9781728141114

With the improvement of the level of industrial intelligence, the traditional Cyber-Physical Systems (CPS) upload a large amount of data to the cloud computing center, which greatly reduces the quality of service (QoS) of network in production workshop, and affects the real-time interaction in the collaborative manufacturing process of each production node in the CPS. To solve this problem, a distributed big data processing model based on edge-cloud collaboration is proposed. Firstly, the distributed edge MEC server is arranged in the production workshop to disperse part of the capacity of data processing from the cloud computing center to the production node;Secondly, the deep self-encoded neural network task inference model is built, and the Neurosurge, a task unloading strategy, is used to automatically divide the network layer into parts that execute in the cloud computing center, which realize the collaborative task inference between the edge and cloud;Finally, through the experiment of tool wear monitoring, the feasibility of data processing based on distributed edge-cloud collaborative model is verified.

关键词： cyber-physical systems edge computing collaborative manufacturing real-time interaction

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ClustCrypt: Privacy-preserving clustering of unstructured big data in the cloud 21

ClustCrypt: Privacy-preserving clustering of unstructured bi...

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21st ieee international conference on High Performance computing and Communications, 17th ieee international conference on Smart City and 5th ieee international conference on data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Zobaed, S.M. Ahmad, Sahan Gottumukkala, Raju Salehi, Mohsen Amini School of Computing and Informatics Informatics Research Institute University of Louisiana at Lafayette Louisiana70504 United States

ISBN: (纸本)9781728120584

Security and confidentiality of big data stored in the cloud are important concerns for many organizations to adopt cloud services. One common approach to address the concerns is client-side encryption where data is encrypted on the client machine before being stored in the cloud. Having encrypted data in the cloud, however, limits the ability of data clustering, which is a crucial part of many data analytics applications, such as search systems. To overcome the limitation, in this paper, we present an approach named ClustCrypt for efficient topic-based clustering of encrypted unstructured big data in the cloud. ClustCrypt dynamically estimates the optimal number of clusters based on the statistical characteristics of encrypted data. It also provides clustering approach for encrypted data. We deploy ClustCrypt within the context of a secure cloud-based semantic search system (S3BD). Experimental results obtained from evaluating ClustCrypt on three datasets demonstrate on average 60% improvement on clusters' coherency. ClustCrypt also decreases the search-time overhead by up to 78% and increases the accuracy of search results by up to 35%. © 2019 ieee.

关键词： big data

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Development and Verification of Multi-class data in Artificial intelligence Model Group Based on Cloud computing 5

Development and Verification of Multi-class Data in Artifici...

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5th ieee international conference on Artificial intelligence and big data, ICAIBD 2022

作者： Xiang, Qian Sichuan Sino-Tech College of Information Technology Chengdu China

ISBN: (纸本)9781665499132

Artificial intelligence (AI) has been widely studied. Due to the increasingly wide application of multi-class data, with the characteristics of rapid arrival, rapid change, huge quantity, and potential infinity, the original classification algorithm for the static database cannot adapt to the reality of multi-class data. In order to achieve reliable and accurate data dependency analysis by loop-level parallelism detection in large programs, this paper firstly designs and implements a parallel Naive Bayes (NB) multiclass data analysis algorithm. then, we adjust the number and weight of classifiers as needed to maintain high classification accuracy and reduce the cost. Meanwhile, this paper establishes an optimal complexity model by realizing the self-organizing control of the data mining process. the results show that the parallel NB classification algorithm can bring excellent performance improvement and good scalability and can meet individuals' performance requirements for massive data classification to a certain extent. © 2022 ieee.

关键词： Cloud computing

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A regression prediction model based on incremental iteration for big industrial data 21

A regression prediction model based on incremental iteration...

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作者： Yu, Yao Yi, Dajiang Tang, Zhuo Ou, Deyin Zeng, Ailing Li, Jingning Hunan University College of Information Science and Engineering Changsha China

ISBN: (纸本)9781728120584

the existence of a large number of data sets produced in the industry increases the need for algorithms that efficiently use resources such as storage space and computational efficiency. One of the challenges that machine learning currently faces is how to handle incremental data, especially in the case where the previous round of data has the same part of data with the current round of data. the shortcomings of existing big industrial data regression prediction models are: (1) there is a large amount of redundancy between batch data;(2) for some iterations, not every element in the working set generated by a single iteration needs to re-participate in the next iteration;In this paper, we introduce a machine learning regression algorithm based on incremental iteration. the overall goal is to reduce memory usage and optimise the computational efficiency of curve fitting in order to achieve high performance when the algorithm occurs on a large data set. the experimental results show that: (1) provides a faster regression prediction algorithm, which significantly improves the calculation efficiency and its cost;(2) provides a method of storage, which saves storage space;the proposed concepts and algorithms combine typical industrial application scenarios with advanced artificial intelligence and have the potential to accelerate the implementation of Industry 4.0. © 2019 ieee.

关键词： Machine learning

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Secure Multimedia data Systems in the Era of Artificial intelligence: Significant Progress and Vision for the Future 5

Secure Multimedia Data Systems in the Era of Artificial Inte...

引用

5th ieee international conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)

作者： thuraisingham, Bhavani Univ Texas Dallas Richardson TX 75083 USA

ISBN: (纸本)9798350323856

Multimedia data systems manage complex data types such as text, images, audio, and video. Extensive research and development efforts in multimedia data management systems have been carried out since the1980s. Furthermore, significant progress has been made on securing such systems. For example, techniques for multilevel security for such systems as well as enforcing access control policies have been developed. More recently, with the advent of big data technologies, data science and Artificial intelligence, there is a need for managing, analyzing, and securing massive data systems with heterogeneous data types. this paper first describes the progress that has been made on securing multimedia data systems and then describes our vision for securing such systems in the era of Artificial intelligence.

关键词： Multimedia data Systems Security and Privacy Machine Learning big data Blockchain Artificial intelligence Quantum computing

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Industrial Cyber Intelligent Control Operating System that Hybrid with IEC 61499 and big data on Edge computing 5

Industrial Cyber Intelligent Control Operating System that H...

引用

5th ieee international conference on Artificial intelligence and big data, ICAIBD 2022

作者： Su, Weibin MacHica, Ivy Kim Xu, Gang He, Zhengfang Kong, Yanchun University of Southeastern Philippines College of Information and Computing Davao Philippines Yunnan Technology and Business University Edge Computing Network Center Kunming China Faculty of Construction Engineering Kunming Metallurgy College Kunming China

ISBN: (纸本)9781665499132

IEC 61499 and big data systems, which are also distributed architecture, but they belong to two independent and unrelated platforms due to different network and control concepts. With the development of intelligent manufacturing factories, the big data generated by the process of industrial production is facing unprecedented challenges of storage and data processing. the current approach is to put industrial big data on cloud computing and the industrial automation control program on the side that is close to physical equipment. this architecture has the problems of low efficiency, insufficient security and poor real-time performance. the purpose of this paper is to design an architecture for intergration IEC61499 and big data. It can not only reduce the cost of building a smart factory, but also increase the security and data access velocity. More importantly, it can meet the real-time performance of industrial control. © 2022 ieee.

关键词： Edge computing

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Anomaly Detection Models for Smart Home Security 5

Anomaly Detection Models for Smart Home Security

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5th ieee Int Conf on big data Security on Cloud (bigdataSecurity) / 5th ieee Int Conf on High Performance and Smart computing (ieee HPSC) / 4th ieee Int Conf on Intelligent data and Security (ieee IDS)

作者： Ramapatruni, Sowmya Narayanan, Sandeep Nair Mittal, Sudip Joshi, Anupam Joshi, Karuna Univ Maryland Baltimore Cty Comp Sci & Engn Baltimore MD 21227 USA

ISBN: (纸本)9781728100067

Recent years have seen significant growth in the adoption of smart homes devices. these devices provide convenience, security, and energy efficiency to users. For example, smart security cameras can detect unauthorized movements, and smoke sensors can detect potential fire accidents. However, many recent examples have shown that they open up a new cyber threat surface. there have been several recent examples of smart devices being hacked for privacy violations and also misused so as to perform DDoS attacks. In this paper, we explore the application of big data and machine learning to identify anomalous activities that can occur in a smart home environment. A Hidden Markov Model (HMM) is trained on network level sensor data, created from a test bed with multiple sensors and smart devices. the generated HMM model is shown to achieve an accuracy of 97% in identifying potential anomalies that indicate attacks. We present our approach to build this model and compare with other techniques available in the literature.

关键词： big data computer network security data privacy hidden Markov models home automation learning (artificial intelligence) sensor fusion big data Hidden Markov models Network security home automation data privacy Sensor fusion Smart homes anomaly detection smoke sensor test bed Smart devices Distributed Denial of Service

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