A distributed authentication and authorization scheme for in-network big data sharing

Big data has a strong demand for a network infrastructure with the capability to support data sharing and retrieval efficiently. Information-centric networking （ICN） is an emerging approach to satisfy this demand, where big data is cached ubiquitously in the network and retrieved using data names. However, existing authentication and authorization schemes rely mostly on centralized servers to provide certification and mediation services for data retrieval. This causes considerable traffic overhead for the secure distributed sharing of data. To solve this problem, we employ identity-based cryptography （IBC） to propose a Distributed Authentication and Authorization Scheme （DAAS）, where an identity-based signature （IBS） is used to achieve distributed verifications of the identities of publishers and users. Moreover, Ciphertext-Policy Attribnte-based encryption （CP-ABE） is used to enable the distributed and fine-grained authorization. DAAS consists of three phases： initialization, secure data publication, and secure data retrieval, which seamlessly integrate authentication and authorization with the in- terest/data communication paradigm in ICN. In particular, we propose trustworthy registration and Network Operator and Authority Manifest （NOAM） dissemination to provide initial secure registration and enable efficient authentication for global data retrieval. Meanwhile, Attribute Manifest （AM） distribution coupled with automatic attribute update is proposed to reduce the cost of attribute retrieval. We examine the performance of the proposed DAAS, which shows that it can achieve a lower bandwidth cost than existing schemes.

Content Centric Networking: A New Approach to Big Data Distribution

In this paper, we explore network architecture anal key technologies for content-centric networking （CCN）, an emerging networking technology in the big-data era. We descrihe the structure anti operation mechanism of tl CCN node. Then we discuss mobility management, routing strategy, and caching policy in CCN. For better network performance, we propose a probability cache replacement policy that is based on cotent popularity. We also propose and evaluate a probability cache with evicted copy-up decision policy.

The Impact of Online Networks and Big Data in Life Sciences

Advances in Information Technology (IT) have enhanced our ability to gather, collect and analyze information from individuals and specific groups of people online. The emergence of online networks has facilitated connections between individuals by leveraging data exchange in a variety of fields. Online networking in life sciences transforms data collection into actionable information that will improve individual and population health, deliver effective therapies and, consequently, reduce the cost of healthcare. These novel tools might also have a direct impact in personalized medicine programs, since the adoption of new products by health care professionals in life sciences and peer-to-peer learning could be improved using social networks and big data analytics. However, one of the main concerns of information exchange online is data privacy. In this article, we will review how online networks and big data analytics are impacting the life sciences sector.

Internet of Vehicles in Big Data Era

As the rapid development of automotive telematics,modern vehicles are expected to be connected through heterogeneous radio access technologies and are able to exchange massive information with their surrounding environment. By significantly expanding the network scale and conducting both real-time and long-term information processing, the traditional Vehicular AdHoc Networks(VANETs) are evolving to the Internet of Vehicles(Io V), which promises efficient and intelligent prospect for the future transportation system. On the other hand, vehicles are not only consuming but also generating a huge amount and enormous types of data, which is referred to as Big Data. In this article, we first investigate the relationship between Io V and big data in vehicular environment, mainly on how Io V supports the transmission, storage, computing of the big data, and how Io V benefits from big data in terms of Io V characterization,performance evaluation and big data assisted communication protocol design. We then investigate the application of Io V big data in autonomous vehicles. Finally, the emerging issues of the big data enabled Io V are discussed.

Decision Model of Knowledge Transfer in Big Data Environment

A decision model of knowledge transfer is presented on the basis of the characteristics of knowledge transfer in a big data environment.This model can determine the weight of knowledge transferred from another enterprise or from a big data provider.Numerous simulation experiments are implemented to test the efficiency of the optimization model.Simulation experiment results show that when increasing the weight of knowledge from big data knowledge provider,the total discount expectation of profits will increase,and the transfer cost will be reduced.The calculated results are in accordance with the actual economic situation.The optimization model can provide useful decision support for enterprises in a big data environment.

Exploring the Big Data Using a Rigorous and Quantitative Causality Analysis

Causal analysis is a powerful tool to unravel the data complexity and hence provide clues to achieving, say, better platform design, efficient interoperability and service management, etc. Data science will surely benefit from the advancement in this field. Here we introduce into this community a recent finding in physics on causality and the subsequent rigorous and quantitative causality analysis. The resulting formula is concise in form, involving only the common statistics namely sample covariance. A corollary is that causation implies correlation, but not vice versa, resolving the long-standing philosophical debate over correlation versus causation. The applicability to big data analysis is validated with time series purportedly generated with hidden processes. As a demonstration, a preliminary application to the gross domestic product (GDP) data of United States, China, and Japan reveals some subtle USA-China-Japan relations in certain periods.

The Roles of 5G Mobile Broadband in the Development of IoT, Big Data, Cloud and SDN

The fast technology development of 5G mobile broadband (5G), Internet of Things (IoT), Big Data Analytics (Big Data), Cloud Computing (Cloud) and Software Defined Networks (SDN) has made those technologies one after another and created strong interdependence among one another. For example, IoT applications that generate small data with large volume and fast velocity will need 5G with characteristics of high data rate and low latency to transmit such data faster and cheaper. On the other hand, those data also need Cloud to process and to store and furthermore, SDN to provide scalable network infrastructure to transport this large volume of data in an optimal way. This article explores the technical relationships among the development of IoT, Big Data, Cloud, and SDN in the coming 5G era and illustrates several ongoing programs and applications at National Chiao Tung University that are based on the converging of those technologies.

E-Healthcare Supported by Big Data

The era of open information in healthcare has arrived. E-healthcare supported by big data supports the move toward greater trans-parency in healthcare by making decades of stored health data searchable and usable. This paper gives an overview the e-health-care architecture. We discuss the four layers of the architecture-data collection, data transport, data storage, and data analysis-as well as the challenges of data security, data privacy, real-time delivery, and open standard interface. We discuss the necessity of establishing an impeccably secure access mechanism and of enacting strong laws to protect patient privacy.

基于大数据聚类的通信网络安全态势预测技术

传统通信网络安全态势预测技术缺乏大数据支撑,难以对发生的攻击进行详细分类和追踪,导致在进行长时间的态势预测中收敛过慢,准确度降低.提出一种基于大数据聚类的通信网络安全态势预测技术.分析通信网络的属性以及特点,选择安全态势描述一级指标,将数据标准化处理之后,细分出二级指标;优化大数据聚类算法,计算最优聚类数量、确定聚类中心,建立关联规则库并优化预测流程,完成基于大数据聚类的通信网络安全态势预测技术的设计.通过实验结果表明,与两种传统的安全态势预测技术相比,设计的技术收敛速度更快,全体数据点没有出现残差扩散的现象,并且数据完整度较高.

大数据与计算模型

当前,人工智能持续升温,大语言模型吸引了众多人士的关注,并在全球范围内掀起了一股热潮。人工智能的成功本质上不是大算力“出奇迹”,而是改变了计算模型。首先,肯定了数据对于人工智能的基础性作用,指出合成数据将是未来数据的主要来源。然后,回顾了计算模型的发展历程,重点介绍了神经网络模型与图灵模型的历史性竞争;指出了大模型的重要标志是机器涌现智能,强调大模型的本质是“压缩”;分析了大模型产生“幻觉”的原因。最后,呼吁科技界在智能化科研中要重视大科学模型。

基于大数据技术的网络安全态势感知平台设计

在网络安全威胁不断演变的背景下,构建一套基于大数据技术的网络安全态势感知平台至关重要。本文介绍了网络安全态势感知平台的设计框架,包括系统架构、数据采集与处理、安全事件检测与分析以及实时可视化等关键模块。通过应用案例分析,突显平台在实际环境中的应用效果。实时可视化与报告和自动化响应与持续改进两个章节详细探讨了用户界面设计、报告定制化和自动响应机制。最后,总结了平台设计的关键要点,并展望了未来网络安全领域的发展趋势。

Artificial Intelligence Self-Organising (AI-SON) Frameworks for 5G-Enabled Networks: A Review

The fifth generation (5G) networks will support the rapid emergence of Internet of Things (IoT) devices operating in a heterogeneous network (HetNet) system. These 5G-enabled IoT devices will result in a surge in data traffic for Mobile Network Operators (MNOs) to handle. At the same time, MNOs are preparing for a paradigm shift to decouple the control and forwarding plane in a Software-Defined Networking (SDN) architecture. Artificial Intelligence powered Self-Organising Networks (AI-SON) can fit into the SDN architecture by providing prediction and recommender systems to minimise costs in supporting the MNO’s infrastructure. This paper presents a review report on AI-SON frameworks in 5G and SDN. The review considers the dynamic deployment and functions of the AI-SON frameworks, especially for SDN support and applications. Each module in the frameworks was discussed to ascertain its relevance based on the context of AI-SON and SDN integration. After examining each framework, the identified gaps are summarised as open issues for future works.

基于微服务和物联网的配电网智能计量架构

为实现电力系统向智能电网模式的演进,依赖于配电网(DN)的自动化,需要新的基础设施、技术和应用。提出一种基于微服务和物联网(IoT)的配电网自动化的智能计量设施,支持各种有助于实现配电网自动化和管理的服务。首先,在测量基础设施中对特定硬件、设备和底层协议进行抽线;然后,利用消息队列遥测传输(MQTT)作为以事件为导向的发布/订阅协议来实现,同时支持发布/订阅和请求/响应机制来实现;最后,使用以大数据为导向的可群集软件模块来实现可扩展性。分布式状态估计(SE)和网络拓扑重构算法验证了智能计量设施支持交互和协同操作。

大数据时代计算机网络信息安全探讨

随着科技的不断发展,大数据时代已经到来,大数据技术广泛用于各行各业,为社会带来巨大的变革。然而,与大数据的快速发展相伴而生的是计算机网络信息安全问题的加剧。大数据时代网络安全问题的复杂性和严重性不容忽视,需要我们认真思考并采取有效措施应对。文章将探讨大数据时代对计算机网络信息安全的挑战,并提出一些关键策略以确保信息安全。

大数据时代人工智能在计算机网络技术中的应用

随着大数据时代的到来,人工智能(AI)在计算机网络技术中的应用已成为推动行业革新的关键因素。基于此,本文探讨了AI在计算机网络技术中的应用背景,分析了AI在计算机网络技术中的具体应用,讨论AI技术应用面临的问题以及未来发展趋势。旨在为网络技术的发展提供一个结合AI应用的新视角,探索在大数据背景下的创新解决方案。

基于BP神经网络模型的矿井生产预警技术研究

矿井生产具有高动态、快时变、混沌和随机特点,生产过程中的人、设备、环境、管理等海量数据存在多源异构特征使得安全风险评估不确定性增加。使用智能算法对空间生产场景进行模拟建模,对影响矿井安全的因素建立预警指标体系并对指标量化与预处理,通过BP神经网络模型对预处理后的指标数据模型训练与测试,测试结果表明,随着迭代次数的增加,模型的收敛性与准确率极大提高,可以最大限度地提供矿井安全风险评估决策所用信息、为提高煤矿安全监测的效率和监测深度提供技术支持。

配电网监测数据索引集群的分布缓存方法

针对配电网监测数据急剧增加导致配电网监控系统响应时间过长的问题,本文提出一种热点数据部分链表分布缓存方法。首先,引入缓存技术并分析原有热点数据链表缓存方法的局限性,提出一种新的缓存方法以提高缓存管理效率;然后,设计预热查询实现索引系统的热启动从而提高索引系统的缓存命中率;最后,以配电网监测数据为算例,验证所提方法的效果。测试结果表明,所提出的缓存方法及设计的预热查询能在不同查询条件下大幅减少索引集群的查询时间,有效提高配电网监测数据索引集群的查询效率。

跨省域智慧能源管理大数据平台架构及关键技术研究与实践

随着广东省能源集团公司下属电站的不断建设,集团公司对各个电站的运营、运维、人员的管理问题愈发凸显。为有效提升各能源场站的综合管理水平,通过搭建基于物联网的智慧能源管理大数据平台对能源场进行集约管理。系统以工业互联网为硬件基础,以大数据平台为软件核心,实现了系统对风电场、光伏电站、煤层气电站等区域的实时远程监控及各项业务管理。实践证明,本系统能减少运行维护成本,提升集团公司的整体核心竞争力。

关于计算机网络安全大数据分析及防范架构探讨

随着我国各项事务电子化发展,计算机网络中各种类型的业务系统快速增多,信息安全面临的挑战也不断提高。为了拥有安全的网络环境,防范各种非授权的数据操作,需要综合使用现有的大数据技术对网络中涉及安全的信息进行采集、聚合、挖掘、分析与管理,由此形成安全态势感知及预警功能,及时反馈给管理者、设备、业务、操作系统进行处理,提高网络安全防范能力。

东南亚恐怖主义网络发动的特征及运行机制研究

当前,东南亚恐怖主义的发展态势呈现出超民族、超国家及超地域的新特点,也出现了跨境犯罪、武装贩毒、人口走私、网络宣传与策划的新动向。在国际恐怖组织利用互联网和自媒体等媒介实施网络发动的新趋势下,东南亚地区更是成为恐怖主义网络发动的主战场之一。恐怖主义网络发动是指恐怖分子利用网络技术进行宣传、招募、组织培训和策动恐怖袭击等活动,可划分为互联网1.0时代的网络破坏、互联网2.0时代的社交媒体利用以及互联网3.0时代的新技术应用。东南亚地区的恐怖主义网络发动具有模仿国际恐怖组织模式、直接进入手机端与高度伪装性等特征。文章深入分析东南亚恐怖主义网络发动的环境、参与主体、驱动力和机制,构建恐怖主义网络发动的过程框架,指出东南亚恐怖主义网络发动研究的重点方向,包括解构典型案例和寻找规律、开发获取和处理关键反恐技术、构建动态监测与预警体系以及建立数据应用平台等。