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摘要

:大数据是不同类别的海量数据集的集合,可分为结构化数据和非结构化数据。当我们在这个计算阶段从千兆/tera/peta/exabytes革新到zeta字节时,威胁也随之增加。除了大型组织,降低成本也是中小型组织使用的标准,从而增加了安全威胁。一次检查流数据不是解决方案,因为安全漏洞无法理解。云中的数据堆栈并不是唯一的首选,因为大数据技术可用于分配结构化和非结构化数据。如今,大量的数据是由移动电话(智能手机)或同样的交响乐形式引起的。大数据架构在为最高消费而设计的移动云中得到了全面的理解。有史以来最好的实现是能够实际地利用MapReduce技术的新颖的以数据为中心的架构,而Hadoop分布式文件系统在使用不同排列的数据方面也承担着巨大的责任。随着时间的推移,来自不同来源的信息和数据的水平得到了提高,执行速度也得到了提高。在本章中,我们的目标是找出易受攻击的大数据安全,并找出最佳的解决方案。我们认为,这一尝试将使我们在朝着更好地接近机会的方向前进的道路上迈出一大步。
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6. Big data security issues with challenges and solutions
: Big data is a collection of huge sets of data with different categories where it could be distinguished as structured and unstructured data. As we are revolutioniz-ing to zeta bytes from Giga/tera/peta/exabytes in this phase of computing, the threats have also increased in parallel. Besides big organizations, cost reduction is the criterion for the use of small- and medium-sized organizations too, thereby increasing the security threat. Checking of the streaming data once is not the solution as security breaches cannot be understood. The data stack up within the clouds is not the only preference as big data technology is available for dispensation of both structured and unstructured data. Nowadays, an enormous quantity of data is provoked by mobile phones (smart-phone) or equally the symphony form. Big data architecture is comprehended among the mobile cloud designed for supreme consumption. The best ever implementation is able to be conked out realistically to make use of a novel data-centric architecture of MapReduce technology, while Hadoop distributed file system also acts with immense liability in using data with divergent arrangement. As time approaches, the level of information and data engendered from different sources enhanced and faster execution is the claim for the same. In this chapter our aim is to find out big data security that is vulnerable and also to find out the best possible solutions for them. We consider that this attempt will dislodge a stride for-ward along the way to an improved evolution in secure propinquity to opportunity.
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Frontmatter 3. Anomaly detection in cloud big database metric 6. Big data security issues with challenges and solutions 1. Introduction 5. Steganography, the widely used name for data hiding
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