围绕大数据分析和安全的新兴趋势:专题讨论

Rafae Bhatti, R. LaSalle, Robert Bird, T. Grance, E. Bertino
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引用次数: 12

摘要

该小组将讨论以大数据分析和安全为中心的主要新兴安全趋势之间的相互作用。随着大数据的爆炸式增长和云计算的出现,数据分析不仅变得普遍,而且成为关键的业务需求。今天的互联网应用程序消耗从异构大数据存储库收集的大量数据,并从中提供有意义的见解。这些应用包括商业预测、投资和金融、医疗保健和福祉、科学和高科技等。安全和运营情报是大数据分析有望发挥关键作用的关键领域之一。大数据环境中的安全分析提出了一系列独特的挑战,而现有的安全事件和事件监控(或SIEM)系统通常无法正确解决这些挑战,这些系统通常使用企业网络中有限的一组传统数据源(防火墙、IDS等)。由于潜在数据源的爆炸式增长和异构性,大数据环境既带来了巨大的机遇,也带来了巨大的挑战,这些数据源将分析的边界扩展到社交网络、实时流和其他形式的高度上下文数据,这些数据以高容量和高速度为特征。除了应对基础设施的挑战外,还有其他未解决的问题,包括但不限于自进化威胁本体的开发、集成网络和应用层分析以及“低而慢”攻击的检测。同时,安全分析需要高度的数据保证,其中保证意味着数据是可信的,并且以保护隐私的方式进行管理。我们的小组成员代表了来自工业界、学术界和政府的个人,他们处于大数据安全分析的前沿。他们将提供对这些独特挑战的见解,调查新兴趋势,并提出未来的愿景。
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Emerging trends around big data analytics and security: panel
This panel will discuss the interplay between key emerging security trends centered around big data analytics and security. With the explosion of big data and advent of cloud computing, data analytics has not only become prevalent but also a critical business need. Internet applications today consume vast amounts of data collected from heterogeneous big data repositories and provide meaningful insights from it. These include applications for business forecasting, investment and finance, healthcare and well-being, science and hi-tech, to name a few. Security and operational intelligence is one of the critical areas where big data analytics is expected to play a crucial role. Security analytics in a big data environment presents a unique set of challenges, not properly addressed by the existing security incident and event monitoring (or SIEM) systems that typically work with a limited set of traditional data sources (firewall, IDS, etc.) in an enterprise network. A big data environment presents both a great opportunity and a challenge due to the explosion and heterogeneity of the potential data sources that extend the boundary of analytics to social networks, real time streams and other forms of highly contextual data that is characterized by high volume and speed. In addition to meeting infrastructure challenges, there remain additional unaddressed issues, including but not limited to development of self-evolving threat ontologies, integrated network and application layer analytics, and detection of "low and slow" attacks. At the same time, security analytics requires a high degree of data assurance, where assurance implies that the data be trustworthy as well as managed in a privacy preserving manner. Our panelists represent individuals from industry, academia, and government who are at the forefront of big data security analytics. They will provide insights into these unique challenges, survey the emerging trends, and lay out a vision for future.
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