Hadoop-based secure storage solution for big data in cloud computing environment

In order to address the problems of the single encryption algorithm,such as low encryption efficiency and unreliable metadata for static data storage of big data platforms in the cloud computing environment,we propose a Hadoop based big data secure storage scheme.Firstly,in order to disperse the NameNode service from a single server to multiple servers,we combine HDFS federation and HDFS high-availability mechanisms,and use the Zookeeper distributed coordination mechanism to coordinate each node to achieve dual-channel storage.Then,we improve the ECC encryption algorithm for the encryption of ordinary data,and adopt a homomorphic encryption algorithm to encrypt data that needs to be calculated.To accelerate the encryption,we adopt the dualthread encryption mode.Finally,the HDFS control module is designed to combine the encryption algorithm with the storage model.Experimental results show that the proposed solution solves the problem of a single point of failure of metadata,performs well in terms of metadata reliability,and can realize the fault tolerance of the server.The improved encryption algorithm integrates the dual-channel storage mode,and the encryption storage efficiency improves by 27.6% on average.

Big Data Access Control Mechanism Based on Two-Layer Permission Decision Structure

Big data resources are characterized by large scale, wide sources, and strong dynamics. Existing access controlmechanisms based on manual policy formulation by security experts suffer from drawbacks such as low policymanagement efficiency and difficulty in accurately describing the access control policy. To overcome theseproblems, this paper proposes a big data access control mechanism based on a two-layer permission decisionstructure. This mechanism extends the attribute-based access control (ABAC) model. Business attributes areintroduced in the ABAC model as business constraints between entities. The proposed mechanism implementsa two-layer permission decision structure composed of the inherent attributes of access control entities and thebusiness attributes, which constitute the general permission decision algorithm based on logical calculation andthe business permission decision algorithm based on a bi-directional long short-term memory (BiLSTM) neuralnetwork, respectively. The general permission decision algorithm is used to implement accurate policy decisions,while the business permission decision algorithm implements fuzzy decisions based on the business constraints.The BiLSTM neural network is used to calculate the similarity of the business attributes to realize intelligent,adaptive, and efficient access control permission decisions. Through the two-layer permission decision structure,the complex and diverse big data access control management requirements can be satisfied by considering thesecurity and availability of resources. Experimental results show that the proposed mechanism is effective andreliable. In summary, it can efficiently support the secure sharing of big data resources.

Evaluating Security of Big Data Through Fuzzy Based Decision-Making Technique

In recent years,it has been observed that the disclosure of information increases the risk of terrorism.Without restricting the accessibility of information,providing security is difficult.So,there is a demand for time tofill the gap between security and accessibility of information.In fact,security tools should be usable for improving the security as well as the accessibility of information.Though security and accessibility are not directly influenced,some of their factors are indirectly influenced by each other.Attributes play an important role in bridging the gap between security and accessibility.In this paper,we identify the key attributes of accessibility and security that impact directly and indirectly on each other,such as confidentiality,integrity,availability,and severity.The significance of every attribute on the basis of obtained weight is important for its effect on security during the big data security life cycle process.To calculate the proposed work,researchers utilised the Fuzzy Analytic Hierarchy Process(Fuzzy AHP).Thefindings show that the Fuzzy AHP is a very accurate mechanism for determining the best security solution in a real-time healthcare context.The study also looks at the rapidly evolving security technologies in healthcare that could help improve healthcare services and the future prospects in this area.

Modified Buffalo Optimization with Big Data Analytics Assisted Intrusion Detection Model

Lately,the Internet of Things(IoT)application requires millions of structured and unstructured data since it has numerous problems,such as data organization,production,and capturing.To address these shortcomings,big data analytics is the most superior technology that has to be adapted.Even though big data and IoT could make human life more convenient,those benefits come at the expense of security.To manage these kinds of threats,the intrusion detection system has been extensively applied to identify malicious network traffic,particularly once the preventive technique fails at the level of endpoint IoT devices.As cyberattacks targeting IoT have gradually become stealthy and more sophisticated,intrusion detection systems(IDS)must continually emerge to manage evolving security threats.This study devises Big Data Analytics with the Internet of Things Assisted Intrusion Detection using Modified Buffalo Optimization Algorithm with Deep Learning(IDMBOA-DL)algorithm.In the presented IDMBOA-DL model,the Hadoop MapReduce tool is exploited for managing big data.The MBOA algorithm is applied to derive an optimal subset of features from picking an optimum set of feature subsets.Finally,the sine cosine algorithm(SCA)with convolutional autoencoder(CAE)mechanism is utilized to recognize and classify the intrusions in the IoT network.A wide range of simulations was conducted to demonstrate the enhanced results of the IDMBOA-DL algorithm.The comparison outcomes emphasized the better performance of the IDMBOA-DL model over other approaches.

Classification of Big Data Security Based on Ontology Web Language

A vast amount of data (known as big data) may now be collected and stored from a variety of data sources, including event logs, the internet, smartphones, databases, sensors, cloud computing, and Internet of Things (IoT) devices. The term “big data security” refers to all the safeguards and instruments used to protect both the data and analytics processes against intrusions, theft, and other hostile actions that could endanger or adversely influence them. Beyond being a high-value and desirable target, protecting Big Data has particular difficulties. Big Data security does not fundamentally differ from conventional data security. Big Data security issues are caused by extraneous distinctions rather than fundamental ones. This study meticulously outlines the numerous security difficulties Large Data analytics now faces and encourages additional joint research for reducing both big data security challenges utilizing Ontology Web Language (OWL). Although we focus on the Security Challenges of Big Data in this essay, we will also briefly cover the broader Challenges of Big Data. The proposed classification of Big Data security based on ontology web language resulting from the protégé software has 32 classes and 45 subclasses.

四川省大豆生产格局变化及驱动因素研究

四川省是我国13个粮食主产区之一,也是我国大豆种植的新兴地区和西南产区的重要组成,研究大豆生产格局对四川省落实粮食安全战略、推动西南地区大豆产业发展具有重大意义。文章基于2000—2020年四川省183个区市县的面板数据,运用空间基尼指数、地理集中度系数、空间转移系数、探索性空间数据分析、最优地理探测器分析了大豆生产的时空格局变化及驱动因素。研究结果发现:1)2000—2020年,四川省大豆产能波动上升,空间分布极不均衡,聚集水平逐步上升,并逐步向川中丘陵区集中;2)大豆生产存在较强的正向空间相关性,总体表现为高-高聚集和低-低聚集;3)资源要素、比较收益、地理气候、经济社会等因素对大豆生产格局变化的影响均高度显著,且呈现非线性增强、双因子增强的交互效应。资源要素投入、比较收益、海拔高程长期以来对大豆生产格局的影响较为显著且呈波动上升趋势,气温、乡村家庭规模的影响力提升较快,交通条件、地区GDP的影响力则总体呈下降趋势,耕作制度长期以来驱动力最弱。基于此,四川省大豆生产应着力破解耕地资源细碎化与劳动力短缺等资源环境约束,大力发展生产性服务业,全面提升大豆生产机械化水平。通过强化科技创新提升川豆单产,并进一步优化大豆生产、农机、服务、保险等环节的政策保障。同时,应重点关注气候变化引发的干旱等自然风险,健全农业领域自然灾害风险预警与防范机制,以进一步强化大豆产业的综合风险抵御能力。

新发展格局下提升产业链供应链韧性与安全的难点与着力点

从“两头在外”“大进大出”模式转向构建以国内大循环为主体、国内国际双循环相互促进的新发展格局,既是对产业链供应链安全风险的积极应对,也对产业链供应链水平提出更高要求。虽然我国已形成完整的产业链条和完善的产业配套,但与世界领先水平相比仍存在差距,包括:产业规模大,但高技术产业比重较低;产业链相对完整,但关键环节薄弱;创新能力增强,但产业链引领力较弱;产业集群优势明显,但产业根植性不强。提升我国产业链供应链韧性与安全面临多重难点,包括产业链外迁压力加大了保持产业链完整性的难度,畅通国内大循环的紧迫性加大了补链强链的难度,美西方国家的脱钩断链加大了产业技术创新难度,国内市场分割加大了形成接断点补短板合力的难度,长期处于赶超阶段加大了在“无人区”锻长板的难度。提升产业链供应链韧性与安全可以从优化要素组合、保持传统制造优势,发挥综合优势、补齐产业链关键短板,发挥超大规模优势、增强产业链引领能力,加强国际合作、优化产业链全球布局等方面着力。

A Practical Study of Big Data Technology in Computer Network Information Security Processing

In recent years,China has witnessed continuous development and progress in its scientific and technological landscape,with widespread utilization of computer networks.Concurrently,issues related to computer network information security,such as information leakage and virus invasions,have become increasingly prominent.Consequently,there is a pressing need for the implementation of effective network security measures.This paper aims to provide a comprehensive summary and analysis of the challenges associated with computer network information security processing.It delves into the core concepts and characteristics of big data technology,exploring its potential as a solution.The study further scrutinizes the application strategy of big data technology in addressing the aforementioned security issues within computer networks.The insights presented in this paper are intended to serve as a valuable reference for individuals involved in the relevant fields,offering guidance on effective approaches to enhance computer network information security through the application of big data technology.

基于“源地-阻力-廊道”的三江源区生态安全格局构建

三江源区是青藏高原生态屏障的重要组成部分,科学构建生态安全格局对筑牢三江源区生态安全屏障、维护可持续发展具有重要意义。耦合层次分析法(AHP)与熵权法,基于生态系统服务、形态学空间格局分析(MSPA)、景观连通性等方法识别不同等级生态源地,利用土地利用类型并综合生态敏感性与地形位指数构建生态阻力面,基于Linkage Mapper工具识别不同类别生态廊道,构建三江源区生态安全格局。结果表明:(1)三江源区生态源地约52371.30km2,占研究区总面积13.70%,多为大尺度不规则斑块,呈现东多西少的空间分布格局;其中一级生态源地面积约为48290.06km2,占生态源地总面积的92.21%,集中分布在生态系统服务重要性较高的中部与东南部地区。(2)识别生态廊道共328条,廊道分布呈现中东部密集,西部稀疏的蜘蛛网状空间格局,整体呈东西向波状延伸态势。(3)识别生态夹点1796km2、生态障碍点2490km2,主要分布于研究区中南部的杂多县;提取生态断裂点61处,集中分布于治多县东南部。(4)构建“三区三带多点”生态安全格局,以生态维育发展区、中部修复关键区、西部生态保护区为“三区”,以绿水青山维护带、生物保护关键带、河源安全建设带为“三带”,识别核心修复点为“多点”,考虑不同小区域内的生态状况,因地制宜进行生态建设。研究结果可以为三江源区生态保护地优化提供科学建议。

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.

新安全格局的基本意涵、历史逻辑与实践路径研究

以中国式现代化全面推进中华民族伟大复兴新征程中,要以新安全格局保障新发展格局。党的十八大以来,从强调“发展”到“统筹发展和安全”再到“新安全格局”的正式提出历经了五个阶段的发展定型,其基本意涵包括国际形势的发展变化之新、发展阶段的历史方位之新、国家安全的形势任务之新、安全格局的建构理念之新、安全格局的内涵要义之新以及安全格局的使命愿景之新六大基本内容。基于历史和现实经验,“新安全格局”是在我国社会主义革命和建设时期、改革开放和社会主义现代化建设时期、中国特色社会主义新时代不断平衡发展和安全的关系问题上逐渐成熟的。面对保障新发展格局的重要任务,加快构建新安全格局要坚持党对国家安全工作的绝对领导、完善国家安全制度体系建设、提升国家安全能力体系建设、完善国家安全基层治理体系、推进完善国际共同安全治理体系。

基于生态系统服务价值评价的松花江流域吉林段生态安全格局构建

为准确识别生态保护的重要区域和生态廊道,进而构建生态安全格局,保障区域生态安全、改善生态环境质量、维持区域可持续发展.以松花江流域吉林段为研究区,基于生态源地-阻力面-生态网络的生态安全格局构建方法,通过InVEST模型评价流域生态系统服务价值,结合自然保护地斑块识别生态源地;选取土地利用和植被覆盖度等数据作为阻力因子,采用熵值法构建综合阻力面;利用linkage mapper工具基于电路理论识别并判定生态廊道、生态节点,构建流域生态安全格局.结果表明:①识别生态源地37块,总面积36212.89 km^(2),呈现东南部集中、中西部分散的空间分布格局;②生态廊道45条,总长度3291.89 km,主要分布在植被覆盖相对较好的地区;③生态节点24个,集中分布在盐碱地、低覆盖度草地等地类.

数智技术赋能统战工作发展透视

数智技术与统战工作在价值理念和政治定位等层面具有高度契合性,前者可以满足后者信息化、智能化、精准化、动态化等发展需求,为后者高质量发展提供持续动能。数智技术能有效支撑和服务于统战工作、开拓统战工作数据应用新空间、激发统战工作新活力。当前我国数智统战工作取得了一些进展,但也面临数字化平台和工具应用不足、数据共享和协同体系不完善以及数智化人才短缺和统战干部理念更新不足等问题。因此,未来数智统战应综合运用数智技术,通过构建面向统战对象的大数据智能分析平台、开发数智化统战工作工具、丰富宣传内容和形式,推动统战工作凝聚人心、汇聚力量;通过制定统一的数智统战顶层设计和规划、构建统一开放的统战云平台、完善数智安全保障体系,赋能大统战工作格局的构建;通过推进协商数智化体系构建、建立政协系统数智人才培训与考核机制、加大新技术在协商业务流程中的应用,深化协商民主。

“大思政课”视域下高校思想政治教育优化探析

“大思政课”建设为新时代高校思想政治教育的优化提供了重要的指引和依托。以“大思政课”理念创新高校思想政治教育的育人格局和思路,是推进高校思想政治教育守正创新和提质增效的现实要求。在深刻把握“大思政课”的内涵价值和逻辑意蕴的基础上,顺应多元文化发展、时代变革要求和社会场域延伸的高校思想政治教育形势发展。善用“大思政课”,在目标上体现大局观与针对性、内容上体现先进性与时效性、手段上体现主导性和协同性、空间上体现社会性与生活性,进一步明确高校思想政治教育优化的具体指向。加强形势教育、培养大局意识,构建协同机制、抓好系统建设,创新教学方式、推动教学改革,丰富教育载体、拓展全域空间,才能在推进和构建“大思政课”体系的过程中优化高校思想政治教育,推进新时代高校思想政治教育系统化、高质量发展。

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。

基于生态安全格局的云南世居民族所在地生态韧性评价研究

云南世居民族所在地是基于传统生态智慧世代延续,以自然系统与社会系统相互嵌套为特点的自然–文化遗产,而现代性和社会经济的快速发展使得世居民族所在地不断被蚕食和消解.研究基于韧性理念,利用多源数据,从稳定性、适宜性、冗余度、多样性4个层面构建云南世居民族所在地生态安全格局和生态韧性耦合框架,对藏、彝、傣族所在地2000—2020年生态韧性进行评价研究.结果表明,近20 a来,世居民族所在地总体生态韧性水平为中等,呈现逐年降低的趋势.高水平韧性区多分布于林地,存在连续但细碎化的分布特征.低水平生态韧性多分布于道路缓冲区2 km以内,路网的社会扰动内涵很大程度上影响生态韧性强度.20 a来平均生态韧性指数傣族所在地(0.6420)>彝族所在地(0.5602)>藏族所在地(0.5344).影响各世居民族所在地生态韧性的原因存在差异,地质灾害防护格局和水生环境安全格局是影响生态韧性的主要因素.

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。

油气储层勘探建模技术新进展及未来展望

油气储层建模利用地质统计学等方法,综合测井、地质、地震等多学科信息,是油气田开发研究的利器,油藏地质模型可以将油藏各种地质特征在三维空间的变化及分布定量表征出来,是油气藏的类型、几何形态、规模、油藏内部结构、储层参数及流体分布的高度概括,储层地质模型是油藏地质模型的核心,可以对储层的沉积特征、非均质性、物性及流体等特征进行综合表征。但在勘探阶段,面对大尺度沉积体系和稀疏井网条件下的储层展布规律表征的建模难点为:①地质知识的量化表达问题,包括地质专家的经验认识如何数字化表征。②稀疏井网条件下无法直接用钻井资料对地质体的发育规模、展布方向和结构特征准确定量描述及构建地质模式,大尺度空间中复杂沉积体系无法用简单数学函数表征。③传统地质统计学等方法在勘探模型构建中如何实现地震、测井、地质、油藏等多维度数据的融合问题。因此,基于确定性建模和传统地质统计学等随机建模的储层建模理论和技术遇到极大挑战。笔者在系统剖析传统储层建模技术流程和方法的基础上,通过构建涵盖地质、测井、地震、分析化验等信息的多学科地学大数据知识库,开展多维数据凝聚层次聚类的沉积相模式库表征和基于生成式网络的智能建模,提出了多学科协同的油气储层勘探建模技术对策及技术体系,实现了构造、沉积及储层之间匹配关系的定量表征。该技术体系在东营凹陷北部陡坡带、洼陷带勘探部署中开展系统应用,构建融合古地貌、古物源、搬运通道、测井及地震属性等多信息的岩相、物性及油气运聚的地质模型,基于模型新范式指导部署井位,支撑了陆相断陷盆地复杂砂砾岩体、页岩油等勘探实践。笔者通过深度剖析东营凹陷北部陡坡带勘探建模实践难点及精度问题,进一步探讨了未来油气储层勘探建模技术发展趋势和应用前景。

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数字经济背景下政府大数据治理体系建设的有效途径研究

基于政府大数据治理的最新研究和发展趋势,探讨了政府大数据治理体系建设面临的数据安全和隐私保护难、法律和政策的滞后、数据质量和标准化不规范、专业人才缺乏以及数据共享和合作的组织文化障碍等难题。针对这些难题,提出了相关策略,包括:建立全面的法律框架以指导大数据治理,加强数据安全措施、改进技术基础设施、更新法律和政策、提高数据质量和标准化、培养专业人才,以及增加公众透明度和信任等。