检索结果-内蒙古大学图书馆

An Efficient parallel Reinforcement Learning Approach to Cross-Layer Defense Mechanism in Industrial Control systems

引用

IEEE TRANSACTIONS ON parallel AND distributed systems 2022年第11期33卷 2979-2990页

作者： Zhong, Kai Yang, Zhibang Xiao, Guoqing Li, Xingpei Yang, Wangdong Li, Kenli Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China Changsha Univ Hunan Prov Key Lab Ind Internet Technol & Secur Changsha 410022 Hunan Peoples R China

The ongoing digitalization enables stable control processes and smooth operations of Industrial Control systems (ICSs). A direct consequence of the highly interconnected architecture of ICSs is the introduced cyber vulnerability and increasing cyber security threats to ICSs. Numerous researches pay attention to the security problem of ICSs. However, most current studies face two challenges. First, the interaction problem between the cyber layer and the physical layer of ICSs may result in incorrect attack response strategies. Second, ICSs are real-time systems, but existing defense decision algorithms based on game theory or reinforcement learning techniques have high computational complexity, which prevents them from making decisions quickly. In this paper, we design a new multi-attribute based method for quantifying rewards and propose a multi-attribute based Q-learning algorithm to resolve the interaction problem. In addition, to overcome the limitation of slow convergence, we develop an effective parallel Q-learning (PQL) algorithm to quickly find the optimal strategy. The experimental results show the effectiveness of the PQL algorithm. Compared with the Q-learning algorithm (QL) and the deep Q-network (DQN) algorithm, our proposed solution can reduce the average completion time by 12.5 to 37 percent.

关键词： Games Q-learning Security Integrated circuit modeling Process control Physical layer Stochastic processes Industrial control system (ICS) interaction multiple attributes parallel q-learning stochastic game

来源：评论

学校读者我要写书评

暂无评论

distributed evidential clustering toward time series with big data issue

引用

EXPERT systems WITH APPLICATIONS 2022年第0期191卷 116279-116279页

作者： Gong, Chaoyu Su, Zhi-gang Wang, Pei-hong You, Yang Southeast Univ Sch Energy & Environm Nanjing 210096 Peoples R China Natl Univ Singapore Sch Comp Singapore 119077 Singapore

To analyze time series data with large volume, most of the existing clustering algorithms focus on data reduction techniques or multi-level strategies. However, the destruction of raw data structure is inevitable, leading to the information loss, even an abnormal and unaccountable clustering result. To tackle above issues, we propose a distributed evidential clustering algorithm that can be directly adopted on the raw big data, and parallelize it under the Apache Spark, which is a processing engine built for sophisticated data analysis. Concretely, in a parallel way, the possibility of becoming a cluster center is first calculated for each data object under the framework of evidence theory. After drawing a decision graph, the cluster centers are determined and then a credal partition of the time series data is derived. Without simplifying the data structure, the proposed algorithm can not only detect the number of clusters, but also describe the ambiguity and uncertainty in memberships of every data object. Experiments on several benchmark datasets and one real-world problem show the strong scalability and well clustering performance of the introduced algorithm.

关键词： Big data distributed calculation Apache spark Evidential clustering Warped dissimilarity measure

来源：评论

学校读者我要写书评

暂无评论

An Intelligent Edge-Cloud Collaborative Framework for Communication Security in distributed Cyber-Physical systems

引用

IEEE NETWORK 2024年第1期38卷 172-179页

作者： Chen, Cen Li, Yangfan Wang, Qinyu Yang, Xulei Wang, Xiaokang Yang, Laurence T. South China Univ Technol Sch Future Technol Guangzhou 510641 Guangdong Peoples R China Pazhou Lab Guangzhou 510335 Guangdong Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha 410083 Hunan Peoples R China ASTAR Inst Infocomm Res Singapore 138632 Singapore Hainan Univ Sch Comp Sci & Technol Haikou Hainan Peoples R China Zhengzhou Univ Sch Comp & Artificial Intelligence Zhengzhou Henan Peoples R China

The rapid growth of IoT (Internet of Things) and smart services facilitate many CPS (Cyber-Physical systems) such as smart health, smart grid and so on. Nevertheless, the communication security issues in CPS are becoming more and more important with the growing complexity of the CPS network and the increasing dependency of critical network infrastructure on cyber-based technologies. In recent years, deep learning technology has shown its superiority in detecting communication security attacks, but its high computational complexity and the massive amount of data generated by IoT devices have brought challenges to traditional cloud computing technology in terms of bandwidth and computing resources. In this paper, we have analyzed the characteristics of heterogeneity and hierarchy in attacks on CPS. We have also analyzed the role of edge intelligence in handling the security of large-scale data communication in CPS. Furthermore, we proposed a CPS communication attack detection framework based on edge cloud collaboration, aiming to improve the parallel efficiency of hardware resources when executing detection tasks. We aim to enhance the intelligence of physical devices and the degree of cloud collaboration, satisfying the real-time processing requirements of large-scale, hierarchical CPS attack detection. Furthermore, through simple simulation experiments, we verified the effectiveness of the proposed edge cloud collaboration framework in CPS attack detection.

关键词： Security Cloud computing Image edge detection Deep learning Collaboration Big Data Servers Cyber Physical System Communication Security Deep Learning Edge-cloud Collaboration

来源：评论

学校读者我要写书评

暂无评论

parallel Physics-Informed Neural Networks & Giza Pyramid Construction Optimization Algorithm for FDIAs Detection in Industrial IoT:ML based Techniques 2

Parallel Physics-Informed Neural Networks & Giza Pyramid Con...

引用

2nd International Conference on Sustainable Computing and Smart systems (ICSCSS)

作者： Reddy, Basi A. Rajkumar, R. Yogesh Bharath Inst Higher Educ & Res Dept CSE Chennai Tamil Nadu India Bharath Inst Higher Educ & Res Dept IT Chennai Tamil Nadu India

ISBN: (纸本)9798350391558;9798350379990

The rapid evolution of IIoT (Industrial Internet of Things) in computing has brought about numerous security concerns, among which is the looming threat of False Data Injection (FDI) attacks. To address these attacks, a study introduces a novel approach called MLBT-FDIA-IIoT (Fault Data Injection Attack Detection in IIoT using parallel Physics-Informed Neural Networks with Giza Pyramid Construction Optimization algorithm). This method makes use of real-time sensor data for attack detection. The data is preprocessed using distributed Set-Membership Fusion Filtering (DSMFF) to remove noise. Then, it is fed into a neural network for classification. Specifically, parallel Physics-Informed Neural Networks (PPINN) are used to distinguish between normal operations and False Data Injection Attacks (FDIAs). However, PPINN lacks optimization methods for accurate detection. To address this, the study proposes the Giza Pyramid Construction Optimization algorithm (GPCOA). This algorithm optimizes the PPINN classifier to detect attacks with more precision. The proposed MLBT-FDIA-IIoT method is implemented using MATLAB and evaluates various metrics such as accuracy, recall, and precision. The results demonstrate significant improvements compared to existing techniques such as MLT-FDI-IIoT, FDIA-FDAS-IIoT, and DCDD-IIoT-FDIA.

关键词： MLBT-FDIA-IIoT DSMFF FDI attacks GPCOA Industrial Internet of Things (IIoT) PPINN

来源：评论

学校读者我要写书评

暂无评论

2023 18th International workshop on Cellular Nanoscale Networks and their Applications, CNNA 2023

2023 18th International Workshop on Cellular Nanoscale Netwo...

引用

18th International workshop on Cellular Nanoscale Networks and their Applications, CNNA 2023

ISBN: (纸本)9798350308921

The proceedings contain 35 papers. The topics discussed include: action recognition via graph convolutional networks for the assisted living of the elderly;the case for asymmetric systolic array floorplanning;performance analysis of unipolar CBRAM in 1D1R configuration towards IMC under realistic conditions;towards dense indoor environmental sensing with LoraWAN;effects of a group exercise on the center of mass of elderly people with a high risk of falling;toward real time processing of radar signals detecting elderly people fall;enabling real-time network intrusion detection through custom hardware architecture;interdisciplinary applications of non-integer order circuits & systems;and A 93nW, analog, parallel-connected threshold classifier for epileptic seizure prediction.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Towards Digitalized and Automated Substations: Implementation of Adaptive Protection Control Unit and Monitoring System for Modern Distribution Networks Under Increased Hosting Capacity of distributed Renewable Sources

Towards Digitalized and Automated Substations: Implementatio...

引用

CIRED 2024 Vienna workshop

作者： Atteya, Ayatte I. El-Zonkoly, Amany Ashour, Hamdy A. Ali, Dallia School of Engineering Robert Gordon University Scotland Aberdeen United Kingdom Department of Electrical and Control Engineering College of Engineering and Technology Arab Academy for Science Technology and Maritime Transport Abu Qir campus Alexandria Egypt

ISBN: (纸本)9781837242672

Maximizing the hosting capacity of modern distribution networks to accommodate more distributed renewable sources is key driver to realise energy system security and Net-Zero carbon goals. Protection systems under increased share of renewable sources became more challenging with diverse topology changes originated from the addition, disconnection, or islanding of distributed generation to secure increasing energy demands. This paper aims to implement an adaptive protection control unit interlinked with an interactive monitoring system to enable the real-world application of smart adaptive protection schemes for modern distribution networks under increased hosting capacity of distributed renewable sources. An adaptive protection control kit is experimentally developed to enable the automatic adjustment of optimal relay settings in response to network topology changes arising from the integration of distributed generation. A set of relay settings are pre-optimized within a small-scale meshed network under possible network topologies and stored offline via a master microcontroller which then uses the online breaker status to identify the corresponding network topology and accordingly adjust the pre-optimized relay settings. A human machine interface is further designed and interlinked with the experimentally developed control kit via a slave microcontroller, providing real-time data of actual current measurements and waveforms together with topological changes and self-adaptation of pre-optimized relay settings. Experimental results showed successful adjustment of pre-optimized relay settings in response to topological changes while achieving coordination time intervals within acceptable limits under the tested network topologies. © The Institution of Engineering & Technology 2024.

关键词： Microcontrollers

来源：评论

学校读者我要写书评

暂无评论

Recent trends in distributed production network scheduling problem

引用

ARTIFICIAL INTELLIGENCE REVIEW 2022年第4期55卷 2945-2995页

作者： Rad, N. Bagheri Behnamian, J. Bu Ali Sina Univ Fac Engn Dept Ind Engn Hamadan Hamadan Iran

The complex problems in the real world, an increase in competition among producers, the advancements in equipment and manufacturing products, the high cost of factory equipment and, etc., have led to the production structure change from a centralized structure to a decentralized one. In recent years, distributed systems have become increasingly important. So in order to adapt to market competition and to respond quickly to changing market demand, there is a need to study this problem. An important aspect of planning in a distributed environment is decentralized production. In such cases, it becomes more important to consider the problem of distributed scheduling. In this regard, this paper provides a systematic literature review on the multi-factory scheduling problems in the past eleven years and report the research gaps. To this end, first, the related research was classified based on the shop environments. Then, after reviewing the existing papers and summarizing them, future researches and emerging research fields of the multi-factory scheduling problem are reported. This review indicates that future research should focus on open shop production environments. The results also show only 4% of the papers focus on the virtual alliance. Therefore, researchers need to consider the virtual alliance in the production network and investigate the participation and competition between the partners in such network. Studying the topic of Industry 4.0 in multi-factory scheduling and subsequently investigating the related topics such as information sharing and real-time data are also the new trends in this field. Considering the complex series-parallel structures in the multi-factory production and defining objective functions related to environmental issues such as reducing pollutants and noise are other suggestions for future studies.

关键词： Multi-factory scheduling distributed production network Systematic review New trends

来源：评论

学校读者我要写书评

暂无评论

NSF/IEEE-TCPP Curriculum on parallel and distributed Computing for Undergraduates - Version II - Big Data, Energy, and distributed Computing 2023

NSF/IEEE-TCPP Curriculum on Parallel and Distributed Computi...

引用

54th Annual ACM SIGCSE Technical Symposium on Computer Science Education (SIGCSE TS)

作者： Prasad, Sushil Weems, Charles Sussman, Alan Gupta, Anshul Estrada, Trilce Vaidyanathan, Ramachandran Ghafoor, Sheikh Kant, Krishna Stunkel, Craig Univ Texas San Antonio San Antonio TX 78249 USA Univ Massachusetts Amherst MA 01003 USA Univ Maryland College Pk MD 20742 USA IBM Res Yorktown Hts NY USA Univ New Mexico Albuquerque NM 87131 USA Louisiana State Univ Baton Rouge LA 70803 USA Tennessee Technol Univ Cookeville TN USA Temple Univ Philadelphia PA 19122 USA NVIDIA St Louis MO USA

ISBN: (纸本)9781450394338

This special session will report on the updated NSF/IEEE-TCPP Curriculum on parallel and distributed Computing released in Nov 2020 by the Center for parallel and distributed Computing Curriculum Development and Educational Resources (CDER). The purpose of the special session is to obtain SIGCSE community feedback on this curriculum in a highly interactive manner employing the hybrid modality and supported by a full-time CDER booth for the duration of SIGCSE. In this era of big data, cloud, and multi- and many-core systems, it is essential that the computer science (CS) and computer engineering (CE) graduates have basic skills in parallel and distributed computing (PDC). The topics are primarily organized into the areas of architecture, programming, and algorithms topics. A set of pervasive concepts that percolate across area boundaries are also identified. Version 1 of this curriculum was released in December 2012. That curriculum guideline has over 140 early adopter institutions worldwide and has been incorporated into the 2013 ACM/IEEE Computer Science curricula. This Version-II represents a major revision. The updates have focused on enhancing coverage related to the topical aspects of Big Data, Energy, and distributed Computing. The session will also report on related CDER activities including a workshop series on a PDC institute conceptualization, developing a CE-oriented version of the curriculum, and identifying a minimal set of PDC topics aligned with ABET's exposure-level PDC requirements. The interested SIGCSE audience includes educators, authors, publishers, curriculum committee members, department chairs and administrators, professional societies, and the computing industry.

关键词： Education Undergraduate Curriculum parallel and distributed Computing Bloom's Classification Learning Outcomes

来源：评论

学校读者我要写书评

暂无评论

Scalable Big Spatial Data Processing with SQL Query Compilation and distributed Morsel-driven parallelism

Scalable Big Spatial Data Processing with SQL Query Compilat...

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Sahni, Rahul Zhang, Xiaozheng Chatterjee, Sudip Ray, Suprio University of New Brunswick Fredericton Canada

ISBN: (纸本)9798350362480

The rapid rise in spatial data volumes from diverse sources necessitate efficient spatial data processing capability. Although most relational databases support spatial extensions of SQL query features, they offer limited scalability. Traditional relational database query processing follows a pull-based (or tuple-at-a-time) model of query processing. This is not efficient for processing large volumes of data. A number of specialized spatial data processing systems were developed that extend cluster computing frameworks, such as Spark and Hadoop. However, these systems are characterized by limited or no support for spatial SQL query execution. The few systems that support SQL querying, suffer from the overheads of the pull-based *** present a compilation-based distributed SQL query processing system. It follows a data-centric query compilation approach that takes a SQL query and generates distributed C++ (UPC++) based physical query plans. The generated code is compiled and executed on a distributed in-memory high performance framework based on the Partitioned Global Address Space (PGAS) paradigm. We also introduce morsel-driven parallelism for scalable spatial query execution in a distributed runtime. We conduct experimental evaluation of our system with two real-world datasets on a number of spatial query workloads. Experimental results demonstrate that our system performs significantly better than a leading spatial big data system Apache Sedona and distributed parallel relational database Citus. © 2024 IEEE.

关键词： Structured Query Language

来源：评论

学校读者我要写书评

暂无评论

ApPLIED 2022 - Proceedings of the 2022 workshop on Advanced Tools, Programming Languages, and PLatforms for Implementing and Evaluating Algorithms for distributed systems

ApPLIED 2022 - Proceedings of the 2022 Workshop on Advanced ...

引用

2022 workshop on Advanced Tools, Programming Languages, and PLatforms for Implementing and Evaluating Algorithms for distributed systems, ApPLIED 2022

ISBN: (纸本)9781450392808

The proceedings contain 10 papers. The topics discussed include: graph neural networks as application of distributed algorithms;cascade: an edge computing platform for real-time machine intelligence;towards an approximation-aware computational workflow framework for accelerating large-scale discovery tasks;DARTS: distributed IoT architecture for real-time, resilient and AI-compressed workflows;drone-truck cooperated delivery under time varying dynamics;a roadmap to post-Moore era for distributed systems;colder than the warm start and warmer than the cold start! experience the spawn start in FaaS providers;QUANTAS: quantitative user-friendly adaptable networked things abstract simulator;exploring the use of strongly consistent distributed shared memory in 3D NVEs;a closer look at detectable objects for persistent memory;a closer look at detectable objects for persistent memory;and research summary: deterministic, explainable and efficient stream processing.

关键词：

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：