大数据时代的企业信息安全保障

大数据蕴藏着巨大的价值信息,但同时其数据安全也面临严峻挑战。本文在分析大数据基本特征的基础上,提出了当前大数据面临的安全挑战,并从大数据的存储、应用和管理等方面阐述了大数据安全的应对策略。     

2021年大数据安全的几个主要变化

一些IT安全经理表示,远程工作将成为2021年网络安全工作需要解决的首要问题.企业在2021年将面临哪些大数据安全挑战?企业如何实施大数据安全变革以解决这些挑战? GetApp公司日前发布了《2020年数据安全状况调查报告》,这是数据安全调查中规模最大的一次.83名IT安全部门经理参与此次调查,该报告除了其他事之项外,还预测了2021年大数据安全最大的挑战.那么企业如何应对这些挑战并实施大数据安全的变革呢?     

大数据安全技术研究

介绍了大数据和大数据安全的基本概念,指出了大数据安全相比传统信息安全的挑战,研究了大数据安全的架构,并重点介绍了大数据安全领域最重要的两类安全问题:大数据处理和大数据隐私,对其中的数据匿名处理、数据脱敏等做了重点研究,介绍了大数据安全的一些最佳实践,以帮助人们更好处理大数据安全问题.     

大数据环境下的数据安全研究

知识经济的时代,大数据独有的潜在价值,正被逐渐发掘。然而,对数据安全的管控,却面临诸多挑战。为此,有必要明辨大数据的本源特征,在这种根基上,提出安全管控的化解路径。从现状看,大数据特有的总环境,包含细分出来的数据存留、应用时段中的数据管控。应当接纳这些视角,对数据安全依循的保护路径,予以审慎查验,提出新思路。     

新形势下政务数据安全管理研究

随着信息技术的蓬勃发展和计算机技术在政府各部门的深入应用，政务数据数量急剧攀升，政务数据应用日渐广泛、共享开放日益迫切，政务数据面临的安全威胁也越来越复杂。政务数据不仅仅关系到数据安全，同时关乎国家信息安全，如何在新形势下做好政务数据安全管理就显得尤为重要。本文通过阐述了政务数据安全管理的重要性，分析了新形势下政务数据安全面临的问题与挑战，并提出建议及措施。     

工业数据安全的挑战与应对策略

随着工业互联网的快速发展和工业数据的大规模收集与应用,工业数据的安全性面临着各种挑战和威胁,如数据泄露、篡改、未经授权访问等,工业数据安全问题愈发凸显。通过梳理工业数据安全现状,分析其所面临的挑战,并提出有效的应对策略,旨在提高工业数据的安全性和可靠性,保护工业企业的利益。     

基于大数据分析技术的数据安全与机器学习研究

随着新技术和新需求以及新应用场景的出现,大数据分析技术的应用愈发广泛,同时基于大数据分析技术的数据安全也面临更多新的挑战。本文首先分析了数据安全在大数据中的重要性,并从大数据分析技术与机器学习之间的关系、基于大数据分析技术的机器学习具体应用两个方面对基于大数据分析技术的机器学习的运用原理展开分析,希望为从业者提供一定的参考。     

大数据背景下智慧校园网络数据安全研究

网络安全治理是大数据时代维护网络安全与信息安全的必然选择。大数据时代智慧校园网络数据安全面临着网络安全受到威胁、信息泄露的问题，因此要积极进行智慧校园信息数据安全治理，保证智慧校园信息数据安全。本文从大数据背景下智慧校园信息数据安全存在的威胁、大数据背景下智慧校园数据安全问题分析、大数据背景下智慧校园网络数据安全治理策略三个方面，研究大数据背景下智慧校园网络数据安全问题。     

创新生态系统视域下的政务数据安全治理体系研究——以山西省为例

本文以山西省为例,着重分析了政务数据治理中面临的挑战。创造性地将创新生态系统理论与政务数据安全工作实践相结合,阐述了创新生态系统视域下的政务数据安全治理的理论逻辑,进而从顶层设计、政务数据安全治理环境域、政务数据安全治理域和政务数据安全治理过程域四个角度提出了政务数据安全治理体系框架——IEGDSG框架。     

政务大数据安全治理体系研究

政务大数据作为数字政府建设的核心要素，蕴藏着难以估量的政治、经济、文化和社会价值，在国家安全战略体系中的地位日益凸显。在复杂的网络环境中，数据安全问题不容忽视，本文从政务大数据全生命周期视角出发，从外部攻击、内部泄露、技术漏洞、管理缺陷四个角度分析了政府数据治理过程中面临的安全挑战；聚焦于“数据采集、数据存储、数据传输、数据处理、数据交换与共享、数据销毁”六个阶段，构建了管理、技术、运营三位一体的政务大数据安全治理体系。     

大数据环境下的数据安全性探讨

随着互联网的高速发展,特别是近年来云计算、物联网等新兴技术的出现,社交网络等服务的广泛应用,人类社会的数据的规模正快速地增长,大数据时代已经到来。如何获取,分析大数据已经成为广泛的问题。但随着带来的数据的安全性必须引起高度重视。本文从大数据的概念和特征说起,阐述大数据面临的安全挑战,并提出大数据的安全应对策略。     

Aeronautical communication transition from analog to digital data: A network security survey

The objective of this paper is to present a comprehensive survey of security challenges in aeronautical data communication networks. The civil aviation industry is currently going through an evolution of the air traffic management system. The aviation communication technologies are progressively shifting towards the use of digital data instead of analog voice for traffic control, airline business, and passenger onboard entertainment systems. This paper illustrates the cause-to-effect chain link starting from the modernization of the aeronautical communication systems and leading to the network security concern in the civil aviation. The general threats to air–ground communication are depicted and then categorized. The paper gives an overview of the civil aviation industry efforts for securing the future aeronautical data communications. The security mechanisms and protocols proposed for this purpose are discussed. Open research issues and challenges that have to be addressed in the security of current and future aeronautical data communication networks are presented in detail. The paper concludes with some improvement directions which can help to address those security issues. This survey can be used as a reference guide to first understand the factors that urge both the research community and the aviation industry to be concerned about network security in future aeronautical data communications. Also, it can be used as a first reading to have a global overview of network security issues, challenges and potential solutions in air–ground communication networks.     

大数据背景下海洋数据管理的挑战与对策

空天地底海洋立体观测技术的飞速发展催生了呈指数级增长的多精度、多频度、大覆盖、多模态的海洋数据。然而,目前的研究主要集中于通用大数据,针对海洋数据的专门研究仍处于起步阶段。海洋数据的时空性、多源多类性、海量性、敏感性等特性为其管理带来了新的挑战。阐述了海洋数据的特点和海洋数据管理的基本架构,探讨了海洋数据在数据存储、数据质量、数据安全等环节面临的挑战,分析了相应的对策,为海洋科学与工程技术的研究提供了重要的参考与依据。     

Developing future human-centered smart cities: Critical analysis of smart city security,Data management,and Ethical challenges

As the globally increasing population drives rapid urbanization in various parts of the world, there is a great need to deliberate on the future of the cities worth living. In particular, as modern smart cities embrace more and more data-driven artificial intelligence services, it is worth remembering that (1) technology can facilitate prosperity, wellbeing, urban livability, or social justice, but only when it has the right analog complements (such as well-thought out policies, mature institutions, responsible governance); and (2) the ultimate objective of these smart cities is to facilitate and enhance human welfare and social flourishing. Researchers have shown that various technological business models and features can in fact contribute to social problems such as extremism, polarization, misinformation, and Internet addiction. In the light of these observations, addressing the philosophical and ethical questions involved in ensuring the security, safety, and interpretability of such AI algorithms that will form the technological bedrock of future cities assumes paramount importance. Globally there are calls for technology to be made more humane and human-centered. In this paper, we analyze and explore key challenges including security, robustness, interpretability, and ethical (data and algorithmic) challenges to a successful deployment of AI in human-centric applications, with a particular emphasis on the convergence of these concepts/challenges. We provide a detailed review of existing literature on these key challenges and analyze how one of these challenges may lead to others or help in solving other challenges. The paper also advises on the current limitations, pitfalls, and future directions of research in these domains, and how it can fill the current gaps and lead to better solutions. We believe such rigorous analysis will provide a baseline for future research in the domain.     

计算机数据安全存储技术及应用

数据安全是计算机安全问题的核心,对于很多具有高度保密要求的单位,安全地存储重要数据,并且在不需要这些数据时作彻底销毁不被他人恢复是至关重要的。本文详细介绍了数据安全存储技术的发展现状,并结合典型的企业信息安全防护体系设计进一步阐述了如何为具有一定保密要求的部门建立可靠的数据安全防护系统。此外,本文从数据存储和销毁的原理上进行了详细分析,并给出了应用软件及案例的测试说明,提出了安全销毁数据的详细解决方法。     

The Study on Evaluation Method of Urban Network Security in the Big Data Era

Big data is an emerging paradigm applied to datasets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time. In a Smarter City, available resources are harnessed safely, sustainably and efficiently to achieve positive, measurable economic and societal outcomes. Most of the challenges of Big Data in Smart Cities are multi-dimensional and can be addressed from different multidisciplinary perspectives. Based on the above considerations, this paper combined the PSR method, the fuzzy logic model and the entropy weight method in an empirical study for feasible urban public security evaluation modeling. The PSR method was used to establish an evaluation index system regarding the essence of public security. The Entropy method was used in the weighing assignment process to verify the objectivity of this modeling. The fuzzy method was used for the quantitative analysis to determine the fuzziness of urban public security.     

云环境下APT攻击的防御方法综述

云计算以其快速部署、弹性配置等特性吸引了大量的组织和机构使用,然而近期出现的高级可持续性威胁(Advanced Persistent Threat,APT)相比传统的网络攻击具有攻击持续性、高隐蔽性、长期潜伏等特性,为实现云平台的信息资产的安全与隐私保护带来了极大的冲击和挑战。因此,如何有效地防护APT对云平台的攻击成为云安全领域亟待解决的问题。在阐述APT攻击的基本概念、攻击流程与攻击方法的基础之上,分析了APT新特性带来的多重安全挑战,并介绍了国内外在APT防护方面的研究进展。随后针对APT的安全挑战,提出了云平台下APT防护的建议框架,该框架融入了事前和事中防御策略,同时利用大数据挖掘综合分析可能存在的APT攻击以及用于事中的威胁定位与追踪。最后,介绍了安全框架中的关键技术的研究进展,分析了现有技术的优势与不足之处,并探讨了未来的研究方向。     

Data provenance in SOA: security, reliability, and integrity

Due to the dynamic nature, such as services composition and evaluation, it is critical for a Service-Oriented Architecture (SOA) system to consider its data provenance, which concerns security, reliability, and integrity of data as they are being routed in the system. In a traditional software system, one focuses on the software itself to determine the security, reliability, and integrity of the software. In an SOA system, however, one also needs to consider origins and routes of data and their impact, i.e., data provenance. This paper first analyzes the unique nature and characteristics of data provenance in an SOA system, particularly related to data security, reliability, and integrity. Then it proposes a new framework for data provenance analysis in an SOA system. Finally, this paper uses an example which illustrates these techniques.     

基于网络的数据存取安全性探讨

1 引言计算机网络以及Internet、Intranet的出现与发展引发了计算机领域新的应用高潮,诸如电子商务、远程教育、远程医疗、数字图书馆。网络计算以及层出不穷的网络服务和网站使得计算机网络由少数专门人员使用成为成千上万广大用户工作与生活不可缺少的助手。但是随之而来的网络黑客、病毒以及网上的恶意攻