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.

Challenges and Solutions of Information Security Issues in the Age of Big Data

Big data has been taken as a Chinese national strategy in order to satisfy the developments of the social and economic requirements and the development of new information technology. The prosperity of big data brings not only convenience to people's daily life and more opportunities to enterprises, but more challenges with information security as well. This paper has a research on new types and features of information security issues in the age of big data, and puts forward the solutions for the above issues: build up the big data security management platform, set up the establishment of information security system and implement relevant laws and regulations.

Privacy Cost Analysis and Privacy Protection Based on Big Data

A comprehensive analysis of the impact privacy incidents on its market value is given.A broad set of instances of the exposure of personal information from a summary of some security mechanisms and the corresponding results are presented. The cumulative effect increases in magnitude over day following the breach announcement, but then decreases. Besides, a new privacy protection property, that is, p-sensitive k-anonymity is presented in this paper to protect against identity disclosure. We illustrated the inclusion of the two necessary conditions in the algorithm for computing a p-k-minimal generalization. Algorithms such as k-anonymity and l-diversity remain all sensitive attributes intact and apply generalization and suppression to the quasi-identifiers. This will keep the data "truthful" and provide good utility for data-mining applications, while achieving less perfect privacy. We aim to get the problem based on the prior analysis, and study the issue of privacy protection from the perspective of the model-benefit.

Fifty-Six Big Data V’s Characteristics and Proposed Strategies to Overcome Security and Privacy Challenges (BD2)

The amount of data that is traveling across the internet today, including very large and complex set of raw facts that are not only large, but also, complex, noisy, heterogeneous, and longitudinal data as well. Companies, institutions, healthcare system, mobile application capturing devices and sensors, traffic management, banking, retail, education etc., use piles of data which are further used for creating reports in order to ensure continuity regarding the services that they have to offer. Recently, Big data is one of the most important topics in IT industry. Managing Big data needs new techniques because traditional security and privacy mechanisms are inadequate and unable to manage complex distributed computing for different types of data. New types of data have different and new challenges also. A lot of researches treat with big data challenges starting from Doug Laney’s landmark paper, during the previous two decades;the big challenge is how to operate a huge volume of data that has to be securely delivered through the internet and reach its destination intact. The present paper highlights important concepts of Fifty-six Big Data V’s characteristics. This paper also highlights the security and privacy Challenges that Big Data faces and solving this problem by proposed technological solutions that help us avoiding these challenging problems.

Privacy-preserving deep learning techniques for wearable sensor-based big data applications

Wearable technologies have the potential to become a valuable influence on human daily life where they may enable observing the world in new ways,including,for example,using augmented reality(AR)applications.Wearable technology uses electronic devices that may be carried as accessories,clothes,or even embedded in the user's body.Although the potential benefits of smart wearables are numerous,their extensive and continual usage creates several privacy concerns and tricky information security challenges.In this paper,we present a comprehensive survey of recent privacy-preserving big data analytics applications based on wearable sensors.We highlight the fundamental features of security and privacy for wearable device applications.Then,we examine the utilization of deep learning algorithms with cryptography and determine their usability for wearable sensors.We also present a case study on privacy-preserving machine learning techniques.Herein,we theoretically and empirically evaluate the privacy-preserving deep learning framework's performance.We explain the implementation details of a case study of a secure prediction service using the convolutional neural network(CNN)model and the Cheon-Kim-Kim-Song(CHKS)homomorphic encryption algorithm.Finally,we explore the obstacles and gaps in the deployment of practical real-world applications.Following a comprehensive overview,we identify the most important obstacles that must be overcome and discuss some interesting future research directions.

Cyber Security-Protecting Personal Data

Many organizations have datasets which contain a high volume of personal data on individuals,e.g.,health data.Even without a name or address,persons can be identified based on the details(variables)on the dataset.This is an important issue for big data holders such as public sector organizations(e.g.,Public Health Organizations)and social media companies.This paper looks at how individuals can be identified from big data using a mathematical approach and how to apply this mathematical solution to prevent accidental disclosure of a person’s details.The mathematical concept is known as the“Identity Correlation Approach”(ICA)and demonstrates how an individual can be identified without a name or address using a unique set of characteristics(variables).Secondly,having identified the individual person,it shows how a solution can be put in place to prevent accidental disclosure of the personal details.Thirdly,how to store data such that accidental leaks of the datasets do not lead to the disclosure of the personal details to unauthorized users.

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。

基于大数据及人工智能技术的计算机网络安全防御

随着信息技术的迅猛发展,计算机网络安全成为国家安全、社会稳定和经济发展的重要基石,然而,计算机网络安全面临的威胁和挑战日益严峻,黑客攻击、数据泄露、网络诈骗等事件频发,不仅给个人和企事业单位带来了巨大损失,也对国家的信息安全构成了严重威胁。基于此,文章探讨基于大数据及人工智能技术的计算机网络安全防御策略,以期为网络安全领域的研究和实践提供有益的参考。

我国粮食安全保障新质生产力发展现状与未来重点方向分析

在我国第一个百年奋斗目标实现之际,我国粮食安全保障战略开始由满足人民“吃得饱”向“吃得好”“吃得健康”进行重大转变。这个重大转变遇到一些挑战和困难,首先要以有限资源满足更高的需求;其次现行技术装备相对落后;再次我国经济价格体系在处理投资者、生产者和消费者之间责任和利益关系方面有待改善。今年全国两会期间,针对大数据(bigdata)、人工智能(AI)、云计算(cloudcomputing)等新兴技术出现,适时提出了“加快发展新质生产力”号召。鉴于此,在提出新质生产力具有“原创”(0到1)、“成形”(1到3)和“成势”(3到n)等3质态演变形式的基础上,探讨了我国粮食安全保障新质生产力5个历史时期发展的特点,认知到新质生产力是助力不同时期国家粮食安全战略落地的大“良方”,但不能简单地“照方取药”,而要结合实际情况,主动瞄准“原创”态(0到1)新质生产力动向,进行“成形”态(1到3)和“成势”态(3到n)新质生产力的谋划和创新。进一步分析了新时期信息和生命科学技术给行业新质生产力带来的机遇和挑战,指出新时期保障国家粮食技术的重点发展方向,包括基于数据和人工智能的储粮监管技术、基于事联管理的粮油产品品质提升技术、基于生命科学(含生物工程技术)的绿色虫霉防治技术以及基于人工智能和机器人的储粮研发技术。

数据安全防护体系在高校校园网中的应用

在高校网络的基本组成结构中,校园网具有保障高校数据信息得以安全稳定传输的重要作用,校园网目前已成为高校师生赖以实现沟通联系的基础设施支撑。目前在网络技术的快速转型以及发展趋势作用下,高校校园网日益存在更加突出的网络数据安全威胁,进而体现全面防范以及科学预测校园网数据安全隐患的必要性。文章主要探讨高校校园网构建数据安全防护体系的实践价值,合理改进校园网现有的数据传输安全保障机制。

基于Hadoop技术的加速器大数据安全存储与高效分析系统设计

为了解决当前加速器控制系统在数据管理方面所面临的海量数据安全存储和高效分析处理的问题,在现有的基础上引入了Hadoop大数据框架,并结合大数据其他相关组件,构建一个分布式数据仓库系统。文章详细阐述了数据仓库的搭建过程,包括软硬件架构以及将数据从现有数据库抽取、转换和加载到数据仓库的方案。特别是针对系统归档数据的存储和分析需求,根据实际应用场景设计一个基于HBase的存储解决方案。在系统部署完成后,进行了吞吐量测试,并与当前使用的传统数据库进行性能对比。测试结果显示,基于Hadoop的数据仓库系统在海量数据存储、高性能查询以及数据分析处理方面都表现出明显的优势。这一改进为加速器控制系统提供了更强大的数据管理和处理能力,有望为加速器在未来的发展提供更多可能性。

大数据背景下网络安全与隐私保护技术研究

随着互联网和大数据技术的发展,网络安全和个人隐私面临着空前的威胁。文章主要介绍大数据、网络安全与个人隐私内容,分析大数据背景下网络安全与隐私保护问题,并探讨大数据背景下网络安全与隐私保护相关的技术,希望对维护网络安全和保护用户隐私有所帮助。

基于大数据的计算机网络安全技术探讨

随着互联网的普及和大数据技术的发展,计算机网络安全问题日益凸显。该文探讨了大数据背景下计算机网络安全技术的应用,以及这些技术在提升网络安全管理水平方面的作用,首先概述了大数据背景下计算机网络安全技术的特点及面临的威胁,接着强调了网络安全管理的重要性,并详细介绍了几种主要的大数据安全应用技术。通过分析可知,这些技术为提高计算机网络安全水平、防范网络攻击提供了有效的手段。

大数据技术在计算机信息安全中的应用

大数据时代,计算机技术被广泛应用于人们的日常生活与工作当中,但技术背后潜藏的安全问题同样值得关注。通过“大数据”这一名词显示出当今信息数据数量之大、传输速度之快,代表了每秒数据呈几何倍数增长的特点,在瞬息万变的环境下容易潜藏安全风险,出现信息被破坏以及泄漏的问题。由此可见,为保证计算机网络信息安全应当分析风险来源并提出针对性的解决办法。

区块链技术在计算机数据安全中的应用

阐述区块链技术在计算机网络安全中的应用,包括保护网络数据信息、智能化管理网上资产、保障通信系统安全稳定。探讨区块链技术在计算机网络数据安全保护中的防范对策,需要完善网络保护配置、建立病毒查杀机制、规范计算机网络操作、增强用户安全管理防护意识。

大数据时代下的医院信息安全技术要点探讨

随着大数据时代的来临,以大数据为基础的云计算技术在社会各个领域得到了越来越多的应用,对社会各领域的发展起到了巨大的推进作用。信息安全技术极大地提高了医院的工作效率,也是医院信息安全工作的重要方法。近几年,随着医疗制度的不断深入,医院的发展也面临着越来越多的挑战和机会。在医院日常信息安全技术中,统计信息的信息安全技术工作是非常重要的一环。它牵扯到了大量的用户数据,将会对医院未来的发展产生重大影响。但是,随着信息技术的发展,人类生活的方方面面都面临着信息安全的各种问题。在大数据环境下,如何提升网络环境中的信息安全水平,保证信息的安全传输和存储,这已成为当前研究的热点问题。通过对医院信息系统(Hospital Information System,HIS)存在的安全隐患和目前HIS的信息安全技术状况进行分析,提出了HIS的安全保障措施,以确保HIS系统稳定、高效、安全地运行。

油气储层用化学封堵材料性能室内评价方法发展方向

目前油气井储层封堵材料室内评价多以承压能力和储层保护能力为主,并以其作为入井依据。但随着技术进步和社会发展,只满足作业需求的某一项或几项指标,无法投入现场试用或应用,导致需要重复检测、评价,造成投入应用时间长,成功率低。从1000余篇国内外油气井安全性、储层保护和环境保护等相关主题词的文献中,发现封得住、稳得起、流得出、过得关和用得起等5项投入应用的性能指标需要检测,并以此为依据将检测方法分为5类。引入大数据思想分析应用成功率与测试指标数量的关系、投入应用时间与测试指标数量的关系发现,评价5类指标比评价单一指标的材料应用成功率最多高62%。5类评价方法项全部完成增加室内工作时间但投入应用的时间最多可节约77%。结果表明,封堵材料应用前评价5类指标,解决了封堵材料投入应用的时间长、效率低难题,不仅为储层封堵材料投入现场应用提供评价指标和检测方法,还为其他井筒作业材料提供了可借鉴的评价手段。

区块链环境下多源大数据访问加密防护仿真

为了提高多源大数据在网络中传输和存储的安全性,提出区块链技术下多源大数据加密防护算法。对多源大数据展开降维处理,减少数据计算量,提高算法的整体效率;结合Henon映射与Logistic映射设计一种双混沌加密算法,实现多源大数据初步加密;利用区块链技术设计多源大数据加密防护方案,在智能合约的基础上,大数据所有者可以将双混沌加密算法加密后的密钥上传至区块链中,同时对数据的有效访问时间展开设定,在有效访问时间内只有满足访问策略的用户才可以访问多源大数据,以此实现多源大数据的加密防护。实验结果表明,所提算法加密后的数据不容易被攻击,且具有较高的运行效率和良好的加密效果。

基于智能合约的教育大数据安全管理和隐私保护算法

传统教育大数据管理面临隐私数据泄露、数据可信度存疑和越权访问等安全风险,为了避免上述风险,提出了一种新型基于智能合约的教育大数据安全管理与隐私保护算法:ASPES(algorithm for security management and privacy protection of education big data based on smart contracts),算法融合了基于Shamir秘密共享的密钥切割改进分享算法、基于SM2-SHA256-AES算法的混合加密算法和基于分层数据访问控制的智能合约管理算法.在真实数据集MOOCCube上的实验结果表明,相较于较先进的方法,ASPES的执行效率和安全性有显著提高,可以有效存储和管理教育大数据,实现教育资源的合理分配.ASPES通过向区块链中嵌入智能合约,将数据读写等操作上链,能够优化管理路径、提高管理效率,保证教育公平,极大地提升教育质量.

大数据技术与个人征信评估:作用机制和实践路径

大数据征信作为社会信用体系构建的关键环节,外延了信用体系的内涵,丰富了信用管理的手段和范围。基于大数据技术使用场景,从信息集成、流程优化和安全维护三个维度对大数据技术应用于个人征信评估的优势展开系统分析,深入探究大数据技术应用于个人征信评估的作用机制,并从加强立法约束、降低数据歧视、注重信息保护三个方面提出大数据技术应用背景下个人征信评估的实践进路,以更好地发挥大数据技术对征信体系及金融领域的赋能作用。