Machine Learning Security Defense Algorithms Based on Metadata Correlation Features

With the popularization of the Internet and the development of technology,cyber threats are increasing day by day.Threats such as malware,hacking,and data breaches have had a serious impact on cybersecurity.The network security environment in the era of big data presents the characteristics of large amounts of data,high diversity,and high real-time requirements.Traditional security defense methods and tools have been unable to cope with the complex and changing network security threats.This paper proposes a machine-learning security defense algorithm based on metadata association features.Emphasize control over unauthorized users through privacy,integrity,and availability.The user model is established and the mapping between the user model and the metadata of the data source is generated.By analyzing the user model and its corresponding mapping relationship,the query of the user model can be decomposed into the query of various heterogeneous data sources,and the integration of heterogeneous data sources based on the metadata association characteristics can be realized.Define and classify customer information,automatically identify and perceive sensitive data,build a behavior audit and analysis platform,analyze user behavior trajectories,and complete the construction of a machine learning customer information security defense system.The experimental results show that when the data volume is 5×103 bit,the data storage integrity of the proposed method is 92%.The data accuracy is 98%,and the success rate of data intrusion is only 2.6%.It can be concluded that the data storage method in this paper is safe,the data accuracy is always at a high level,and the data disaster recovery performance is good.This method can effectively resist data intrusion and has high air traffic control security.It can not only detect all viruses in user data storage,but also realize integrated virus processing,and further optimize the security defense effect of user big data.

Research and application of key technologies for data delivery in railway engineering design based on metadata

In view of the problems of inconsistent data semantics,inconsistent data formats,and difficult data quality assurance between the railway engineering design phase and the construction and operation phase,as well as the difficulty in fully realizing the value of design results,this paper proposes a design and implementation scheme for a railway engineering collaborative design platform.The railway engineering collaborative design platform mainly includes functional modules such as metadata management,design collaboration,design delivery management,model component library,model rendering services,and Building Information Modeling(BIM)application services.Based on this,research is conducted on multi-disciplinary parameterized collaborative design technology for railway engineering,infrastructure data management and delivery technology,and design multi-source data fusion and application technology.The railway engineering collaborative design platform is compared with other railway design software to further validate its advantages and advanced features.The platform has been widely applied in multiple railway construction projects,greatly improving the design and project management efficiency.

Big Metadata,Smart Metadata,and Metadata Capital:Toward Greater Synergy Between Data Science and Metadata

Purpose： The purpose of the paper is to provide a framework for addressing the disconnect between metadata and data science. Data science cannot progress without metadata research.This paper takes steps toward advancing the synergy between metadata and data science, and identifies pathways for developing a more cohesive metadata research agenda in data science. Design/methodology/approach： This paper identifies factors that challenge metadata research in the digital ecosystem, defines metadata and data science, and presents the concepts big metadata, smart metadata, and metadata capital as part of a metadata lingua franca connecting to data science. Findings： The ＂utilitarian nature＂ and ＂historical and traditional views＂ of metadata are identified as two intersecting factors that have inhibited metadata research. Big metadata, smart metadata, and metadata capital are presented as part ofa metadata linguafranca to help frame research in the data science research space. Research limitations： There are additional, intersecting factors to consider that likely inhibit metadata research, and other significant metadata concepts to explore. Practical implications： The immediate contribution of this work is that it may elicit response, critique, revision, or, more significantly, motivate research. The work presented can encourage more researchers to consider the significance of metadata as a research worthy topic within data science and the larger digital ecosystem. Originality/value： Although metadata research has not kept pace with other data science topics, there is little attention directed to this problem. This is surprising, given that metadata is essential for data science endeavors. This examination synthesizes original and prior scholarship to provide new grounding for metadata research in data science.

A Metadata Reconstruction Algorithm Based on Heterogeneous Sensor Data for Marine Observations

Vast amounts of heterogeneous data on marine observations have been accumulated due to the rapid development of ocean observation technology.Several state-of-art methods are proposed to manage the emerging Internet of Things(IoT)sensor data.However,the use of an inefficient data management strategy during the data storage process can lead to missing metadata;thus,part of the sensor data cannot be indexed and utilized(i.e.,‘data swamp’).Researchers have focused on optimizing storage procedures to prevent such disasters,but few have attempted to restore the missing metadata.In this study,we propose an AI-based algorithm to reconstruct the metadata of heterogeneous marine data in data swamps to solve the above problems.First,a MapReduce algorithm is proposed to preprocess raw marine data and extract its feature tensors in parallel.Second,load the feature tensors are loaded into a machine learning algorithm and clustering operation is implemented.The similarities between the incoming data and the trained clustering results in terms of clustering results are also calculated.Finally,metadata reconstruction is performed based on existing marine observa-tion data processing results.The experiments are designed using existing datasets obtained from ocean observing systems,thus verifying the effectiveness of the algorithms.The results demonstrate the excellent performance of our proposed algorithm for the metadata recon-struction of heterogenous marine observation data.

A Novel Metadata Based Multi-Label Document Classification Technique

From the beginning,the process of research and its publication is an ever-growing phenomenon and with the emergence of web technologies,its growth rate is overwhelming.On a rough estimate,more than thirty thousand research journals have been issuing around four million papers annually on average.Search engines,indexing services,and digital libraries have been searching for such publications over the web.Nevertheless,getting the most relevant articles against the user requests is yet a fantasy.It is mainly because the articles are not appropriately indexed based on the hierarchies of granular subject classification.To overcome this issue,researchers are striving to investigate new techniques for the classification of the research articles especially,when the complete article text is not available(a case of nonopen access articles).The proposed study aims to investigate the multilabel classification over the available metadata in the best possible way and to assess,“to what extent metadata-based features can perform in contrast to content-based approaches.”In this regard,novel techniques for investigating multilabel classification have been proposed,developed,and evaluated on metadata such as the Title and Keywords of the articles.The proposed technique has been assessed for two diverse datasets,namely,from the Journal of universal computer science(J.UCS)and the benchmark dataset comprises of the articles published by the Association for computing machinery(ACM).The proposed technique yields encouraging results in contrast to the state-ofthe-art techniques in the literature.

Real-Time Spammers Detection Based on Metadata Features with Machine Learning

Spammer detection is to identify and block malicious activities performing users.Such users should be identified and terminated from social media to keep the social media process organic and to maintain the integrity of online social spaces.Previous research aimed to find spammers based on hybrid approaches of graph mining,posted content,and metadata,using small and manually labeled datasets.However,such hybrid approaches are unscalable,not robust,particular dataset dependent,and require numerous parameters,complex graphs,and natural language processing(NLP)resources to make decisions,which makes spammer detection impractical for real-time detection.For example,graph mining requires neighbors’information,posted content-based approaches require multiple tweets from user profiles,then NLP resources to make decisions that are not applicable in a real-time environment.To fill the gap,firstly,we propose a REal-time Metadata based Spammer detection(REMS)model based on only metadata features to identify spammers,which takes the least number of parameters and provides adequate results.REMS is a scalable and robust model that uses only 19 metadata features of Twitter users to induce 73.81%F1-Score classification accuracy using a balanced training dataset(50%spam and 50%genuine users).The 19 features are 8 original and 11 derived features from the original features of Twitter users,identified with extensive experiments and analysis.Secondly,we present the largest and most diverse dataset of published research,comprising 211 K spam users and 1 million genuine users.The diversity of the dataset can be measured as it comprises users who posted 2.1 million Tweets on seven topics(100 hashtags)from 6 different geographical locations.The REMS’s superior classification performance with multiple machine and deep learning methods indicates that only metadata features have the potential to identify spammers rather than focusing on volatile posted content and complex graph structures.Dataset and REMS’s codes are available on GitHub(www.github.com/mhadnanali/REMS).

WebGIS中地理空间Metadata管理系统研究与设计

为实现 Web GIS中地理空间信息的有效管理和网络共享 ,结合国际地理空间信息共享的标准规范 ,对Web GIS中地理空间 Metadata的定义、作用、内容、技术实现及其应用进行了详细的研究和设计 ,提出了与Web GIS集成的地理空间 Metadata管理系统解决方案 ,并给出了体现具体技术实现的原型系统 ,为国家空间信息基础设施和数字地球的建设提供了理论基础和技术原型 ,在当今信息化与数字地球时代 。

数字地球建设中的地理空间Metadata研究

为解决数字地球建设中现有海量地理空间信息的共建共享、有效管理和利用 ,结合国际地理空间Metadata标准规范 ,对数字地球建设中所需的地理空间Metadata的定义、作用、分类、内容、实现模型与实例及其应用进行详细研究和设计 ,提出用于数字地球建设的地理空间Metadata网络管理系统解决方案 ,并设计实现了原型系统 ,为数字地球建设中现有海量地理空间信息的有效管理和共建共享提供了理论基础和切实可行的技术支撑。对国家空间信息基础设施建设与数字地球建设具有重要的推动作用和深远的社会意义。

Ontology and metadata for online learning resource repository management based on semantic web

An ontology and metadata for online learning resource repository management is constructed. First, based on the analysis of the use-case diagram, the upper ontology is illustrated which includes resource library ontology and user ontology, and evaluated from its function and implementation; then the corresponding class diagram, resource description framework （RDF） schema and extensible markup language （XML） schema are given. Secondly, the metadata for online learning resource repository management is proposed based on the Dublin Core Metadata Initiative and the IEEE Learning Technologies Standards Committee Learning Object Metadata Working Group. Finally, the inference instance is shown, which proves the validity of ontology and metadata in online learning resource repository management.

Metadata:组织和发现Internet信息资源的数据

ｍｅｔａｄａｔａ是“关于数据的数据”，本文介绍了ｍｅｔａｄａｔａ的基本情况，并对ＨＴＭＬ和ＸＭＬ环境的 几个ｍｅｔａｄａｔａ规范进行了论述（包括 Ｄｕｂｌｉｎ ｃｏｒｅ，ＰＩＣＳ，Ｗｅｂ Ｃｏｌｌｅｃｔｉｏｎｓ，ＣＤＦ，ＭＣＦ及ＲＤＦ）。由于ｍｅｔａｄａｔａ 在Ｉｎｔｅｒｎｅｔ信息资源的组织和发现方面起着非常重要的作用，作者呼吁国人应当加强对ｍｅｔａｄａｔａ的研究。

Metadata初探

随着计算机技术与网络通信的进步 ,人类社会迈进了崭新的信息网络时代 ,电子图书馆相关计划普遍受到了各界的重视。而构建电子图书馆一个必要的步骤是将现有的馆藏、出版品经过电子化处理 ,并提供信息的检索、查询、浏览以及传递。在电子化转换的过程中需要适合在网络环境中有效管理电子资源、并能呈现不同馆藏特色的资源描述格式。 Metadata,这个新的电子资源描述格式 (或称电子资源组织模式 ) ,已成为发展电子图书馆计划首要考虑的问题。本文将就 Metadata电子资源描述格式作简略的介绍。

Information Description of Vegetable Planting Metadata Model

[Objective] To study the information description of vegetable planting metadata model. [Method] On the basis of analyzing the data involved in every as- pect of vegetable planting, this paper put forward description schemes of vegetable planting metadata and constructed vegetable planting metadata model by the means of XML/XML schema. [Result] Metadata model of vegetable planting was established, and information description of vegetable planting metadata model was realized by the using of XML Schema. The whole metadata model consists of 7 first-class classifica- tions, including more than 800 information description points which could completely record vegetable planting-related information. [Conclusion] Standards for data collec- tion, management and sharing were provided for the agriculture applications in indus- tries like GAP management of vegetable planting, facility vegetable, food quality traceability, etc.

Metadata与数字典藏

Metadata在数字资料的典藏中起着极其重要的作用。本文试图从数字典藏的策略及 Metadata对数字典藏的重要性 ,进一步探讨用于数字典藏的

Metadata:组织和发现Internet信息资源的数据

metadata是“关于数据的数据”,本文介绍了 m etadata的基本情况 ,并对 HTML 和 XML 环境的几个 m eta-data规范进行了论述 (包括 Dublin core,PICS,Web Collections,CDF ,MCF及 RDF)。由于 metadata在 Internet信息资源的组织和发现方面起着非常重要的作用 ,作者呼吁国人应当加强对 metadata的研究。

数字人体空间数据的Metadata

笔者首先介绍了Metadata的发展背景和现状 ;然后研究了数字人体空间数据Metadata的理论基础、体系结构 ;最后阐述了数字人体空间数据Metadata的实现和今后研究的趋势。

基于Metadata crosswalks的青藏铁路元数据系统设计

人们对数据的需求随着所要研究问题复杂程度的升高而变得复杂多样。青藏铁路地理信息系统数据库管理着不同来源、异质异构的海量数据,元数据在这样的大型系统中扮演着非常重要的角色。元数据分为两类,一类是地理空间元数据,用来描述青藏铁路沿线基础地理背景数据、地形数据、铁路工程数据、与空间相关的冻土和冻土工程地质数据,及数据库运行过程中产生的不同版本的空间数据等;一类是非地理空间元数据,包括属性数据和事务性数据,用来描述钻孔资料、项目研究论文、野外观测资料等。两类数据采用不同的元数据标准,需要引入Metadatacrosswalks技术以解决不同标准元数据间的集成访问,从而为数据用户提供统一高效的元数据检索服务。XML技术是实现这种机制的最佳选择。探讨了Metadatacrosswalks在青藏铁路地理信息元数据库系统中的适用性并对其进行了初步的设计。

基于XML/Metadata的非物质文化数字图书馆建设

鉴于数字图书馆在非物质文化保护中的重要作用,在分析数字图书馆的定义、功能和实现标准的基础上,提出了一种基于XML/Metadata框架的非物质文化数字图书馆架构,并且给出了该架构的组成以及各组成部分的功能划分,描绘了实现各部分功能的关键技术。

Metadata初探

构建电子图书馆一个必要的步骤是将现有的馆藏、出版物经过电子化处理，并提供信息的检索、查询、浏览以及传递，在电子化转换的过程中，需要适合在网络环境中有效管理电子资源，并能呈现不同馆藏特色的资源描述格式。Metadata这个新的电子资源描述格式（或称电子资源组织模式），已成为发展电子图书馆计划首要考虑的问题，本文就Metadata电子资源描述格式作简略的介绍。

Metadata——数字图书馆信息资源的整序

从元数据的概念和功能出发 ,介绍了几种常用元数据格式的特点、用途、和适用范围 ,提出用DC元数据对网络资源进行编目的可行性。

Metadata的涵义

本文从 Webster 字典对 meta 一词的释义出发,列举若干实例,总结metadata 一词的涵义;同时就 metadata 一词的引伸意义作了阐释,并对"元搜索引擎"这种译法提出了不同观点。