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.

Measuring urban sprawl in Beijing with geo-spatial indices

Concerning about the rapid urban growth in recent China, this study takes Beijing as a case and puts forward that urban sprawl can be measured from spatial configuration, urban growth efficiency and external impacts, and then develops a geo-spatial indices system for measuring sprawl, a total of 13 indicators. In order to calculate these indices, different sources data are selected, including land use maps, former land use planning, land price and floor-area-ratio samples, digitized map of the highways and city centers, population and GDP statistical data, etc. Various GIS spatial analysis methods are used to spatialize these indices into 100mx100m cells. Besides, an integrated urban sprawl index is calculated by weight sum of these 13 indices. The application result indicates that geo-spatial indices system can capture most of the typical features and interior differentia of urban sprawl. Construction land in Beijing has kept fast growing with large amount, low efficiency and disordered spatial configuration, indicating a typical sprawling tendency. The following specific sprawl features are identified by each indicator： （1） typical spatial configuration of sprawling： obvious fragmentation and irregularity of landscape due to unsuccessful enforcement of land use planning, unadvisable pattern of typical discontinuous development, strip development and leapfrog development; （2） low efficiency of sprawl： low development density, low population density and economic output in newly developed area; and （3） negative impacts on agriculture, environment and city life. According to the integrated sprawl index, the sprawling amount in the northern part is larger than that in the southern, but the sprawling extent is in converse case; most sprawling area include the marginal area of the near suburbs and the area between highways, etc. Four sprawling patterns are identified： randomly expansion at urban fringe, strip development along or between highways, scattered development of industrial land, leapfrog development of urban residence and industrial area.

An Optimal Method for High-Resolution Population Geo-Spatial Data

China' Mainland has a poor distribution of meteorological stations.Existing models’estimation accuracy for creating high-resolution surfaces of meteorological data is restricted for air temperature,and low for relative humidity and wind speed(few studies reported).This study compared the typical generalized additive model(GAM)and autoencoder-based residual neural network(hereafter,residual network for short)in terms of predicting three meteorological parameters,namely air temperature,relative humidity,and wind speed,using data from 824 monitoring stations across China’s mainland in 2015.The performance of the two models was assessed using a 10-fold cross-validation procedure.The air temperature models employ basic variables such as latitude,longitude,elevation,and the day of the year.The relative humidity models employ air temperature and ozone concentration as covariates,while the wind speed models use wind speed coarse-resolution reanalysis data as covariates,in addition to the fundamental variables.Spatial coordinates represent spatial variation,while the time index of the day captures time variation in our spatiotemporal models.In comparison to GAM,the residual network considerably improved prediction accuracy:on average,the coefficient of variation(CV)R2 of the three meteorological parameters rose by 0.21,CV root-mean square(RMSE)fell by 37%,and the relative humidity model improved the most.The accuracy of relative humidity models was considerably improved once the monthly index was included,demonstrating that varied amounts of temporal variables are crucial for relative humidity models.We also spoke about the benefits and drawbacks of using coarse resolution reanalysis data and closest neighbor values as variables.In comparison to classic GAMs,this study indicates that the residual network model may considerably increase the accuracy of national high spatial(1 km)and temporal(daily)resolution meteorological data.Our findings have implications for high-resolution and high-accuracy meteorological parameter mapping in China.

Overcoming Object Misalignment in Geo-Spatial Datasets

In integrating geo-spatial datasets, sometimes layers are unable to perfectly overlay each other. In most cases, the cause of misalignment is the cartographic variation of objects forming features in the datasets. Either this could be due to actual changes on ground, collection, or storage approaches used leading to overlapping or openings between features. In this paper, we present an alignment method that uses adjustment algorithms to update the geometry of features within a dataset or complementary adjacent datasets so that they can align to achieve perfect integration. The method identifies every unique spatial instance in datasets and their spatial points that define all their geometry;the differences are compared and used to compute the alignment parameters. This provides a uniform geo-spatial features’ alignment taking into consideration changes in the different datasets being integrated without affecting the topology and attributes.

Coastline Security Engineering Using Geo-Spatial Approach: Case of Chabahar Port, Iran

Regarding the special potential of ports located on international coastlines such as Makoran Sea (Iran) for goods and human smuggling, national level of coastline security is very important. They can play a significant role in the development of power and security. Based on military reviews and analyses, police location and monitoring field view in the coastlines are strategic issues in modern security development. This research proposes a tool for development of coastal roads and coastal walking routes in the deployment of police. The main focuses are monitoring field view and accessibility to the strategic coastline. GIS tool plays an essential role in producing important security maps. Chabahar Port in Iran, as the most important port of Makoran Sea, has been selected as the study area, regarding its strategic role in the national economy and security. Research method focused on these major axes: successful establishment of police stations in shoreline for increasing monitoring and coastal security and suitable patrol of patrol police car in the coastal roads. This study adopts a scientific approach to the analysis of the present and future development in urban and security planning in coastal towns in the national and regional levels.

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).

《中医药数据资源元数据》规范

《中医药数据资源元数据》的发布可为中医药数据资源提供元数据著录规范。通过对国内外相关元数据标准的调研及研究,结合中医药数据资源实际情况,在广泛征求专家意见基础上,制定本规范。本规范规定了中医药数据资源元数据结构,包括元数据子集、元数据实体及元数据元素,用于完整描述中医药数据资源,适用于中医药数据资源的编目、归档、建库、发布、共享、交换与检索等,可以帮助用户从海量数据中发现符合需求的资源,以促进中医药领域数据资源描述的规范化和标准化。

基于MDR2023的元数据值域语义约束注册标准化模型研究

元数据注册(MetadataRegistry,MDR)是数据治理中元数据精确表达语义的必要前提。通过全面系统地分析国内外的MDR系统,发现国内外MDR更多关注基本数据元素,数据语义约束注册方面的研究缺乏。因此,首先基于ISO/IEC11179:2023(MDR2023)系列标准提出元数据语义约束外延分类模型,明确元数据语义约束范围,并选取其中的值域语义约束详细研究;其次,基于MDR2023标准提出元数据值域语义约束注册元模型,为元数据语义约束注册提供标准化且完整的注册算法流程,从而为元数据值域语义约束注册提供解决方案;最后,以石油领域著名的POSC标准为需求背景,对其中的值域语义约束进行注册,据此实现石油领域元数据值域语义约束的标准化,验证提出的元数据值域语义约束注册元模型的合理性和可行性。提出的元模型对于其他领域数据治理具有普适性。

基于语义的地方志资源聚合与可视化研究

论文应用语义网相关技术开展地方志资源聚合研究,挖掘海量、分布式、异构资源间的潜在关联,系统分析了基于语义的三种信息资源深度聚合模式特点,并以东北师范大学图书馆“东北地方志”馆藏资源作为数据源,开展基于元数据的地方志资源聚合实证研究。构建元数据视角下地方志资源聚合的理论框架和可视化服务平台,为地方志资源的深度关联和揭示提供从理论到实践的实现路径。

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。

自然环境试验元数据体系研究

目的形成自然环境试验元数据体系,为数据资源提供完备的描述及合理的规则规范,为数据库设计开发、数据挖掘分析、数据共建共享等提供支持。方法针对自然环境试验专业特点、数据类型以及应用需求,借鉴其他领域的元数据标准规范,梳理分析自然环境试验涉及的数据特征元素集合,划分自然环境试验元数据类别,评估元数据作用,形成自然环境试验元数据体系。结果形成了覆盖数据集描述信息、要素参数信息、数据质量信息、维护信息、限制信息、数据集分发信息、元数据参考信息、数据应用成果信息共8个类别的自然环境试验元数据体系。结论自然环境试验元数据具有资源描述、数据选择、信息检索、管理溯源、解释分析、储存入库等作用,为数据有效管理和共建共享提供了基础。本文提出的元数据体系为自然环境试验元数据标准提供了参考思路。

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。

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