大数据安全可视化

Waqar Ahmed, Uzair Hashmi
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引用次数: 0

摘要

到目前为止,2012年全球产生了2.5 qb的数据(1后18个0)。每天的数据创建规模都在变得越来越大,比人类诞生以来的任何时候都要大。简要的数据生成历史分类如下:5pb:流经沃尔玛事务数据库的数据。消费者每天在网上购物上花费27.2万美元。苹果每分钟的应用下载量约为4.7万次。在Facebook上,品牌每分钟会收到超过34000个“赞”。每天有80亿条电子邮件在Twitter上每天有3.4亿条推文在Facebook上每天有68.4万个内容每分钟在Flicker上上传3125张新照片。随着数据规模的增加,安全威胁也在增加,其中包括未经授权的大数据修改/更改。在如此大规模的数据上手动执行安全可视化是无法压缩的。因此,我们需要一些自动化的、易于使用的、节省时间的技术,可以给出全面的结果,这有助于跟踪大数据的完整性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Security Visualization on Big Data
IN 2012 2.5 QB OF DATA (18 ZEROS AFTER 1) WAS GENERATED WORLDWIDE KNOW SO FAR. EVERY DAY DATA CREATION SIZE IS BECOMING BIG-TO-BIGGER THAN WAS SEEN BY EVERYONE SINCE THE BEGINNING OF HUMANKIND. BRIEF DATA GENERATION HISTORY IN CATEGORICAL FASHION IS AS FOLLOWS: 5 Petabytes: Data flowing through Walmart’s transactional databases. Consumers spend $272,000 on Web shopping /day. Apple receives around 47,000 app downloads /minute. On Facebook, Brands receive more than 34,000 “likes” /minute. 8 billion Email messages per day On Twitter 340 million tweets per day On Facebook 684,000 bits of content per day 3,125 new photos uploaded on Flicker per minute. As data size increased so are security threats, which comprise unauthorized modification/ alteration of big data. Conducting security visualization manually on such a large-scale data is beyond compression. Therefore, we need some automated easy to use, time saving technique that can give comprehensive results, which can help to track the integrity of big data.
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