QuickAdapt: Scalable Adaptation for Big Data Cyber Security Analytics

Faheem Ullah, M. Babar
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引用次数: 2

Abstract

Big Data Cyber Security Analytics (BDCA) leverages big data technologies for collecting, storing, and analyzing a large volume of security events data to detect cyber-attacks. Accuracy and response time, being the most important quality concerns for BDCA, are impacted by changes in security events data. Whilst it is promising to adapt a BDCA system's architecture to the changes in security events data for optimizing accuracy and response time, it is important to consider large search space of architectural configurations. Searching a large space of configurations for potential adaptation incurs an overwhelming adaptation time, which may cancel the benefits of adaptation. We present an adaptation approach, QuickAdapt, to enable quick adaptation of a BDCA system. QuickAdapt uses descriptive statistics (e.g., mean and variance) of security events data and fuzzy rules to (re) compose a system with a set of components to ensure optimal accuracy and response time. We have evaluated QuickAdapt for a distributed BDCA system using four datasets. Our evaluation shows that on average QuickAdapt reduces adaptation time by 105× with a competitive adaptation accuracy of 70% as compared to an existing solution.
QuickAdapt:大数据网络安全分析的可扩展适应
大数据网络安全分析(BDCA)是利用大数据技术收集、存储和分析大量的安全事件数据,以检测网络攻击。准确性和响应时间是BDCA最重要的质量关注点,它们受到安全事件数据变化的影响。虽然BDCA系统的体系结构有望适应安全事件数据的变化,以优化准确性和响应时间,但考虑体系结构配置的大搜索空间也很重要。在大的配置空间中寻找潜在的适应会导致压倒性的适应时间,这可能会抵消适应的好处。我们提出了一种适应方法,QuickAdapt,以实现BDCA系统的快速适应。QuickAdapt使用安全事件数据的描述性统计(例如,均值和方差)和模糊规则来(重新)组成一个具有一组组件的系统,以确保最佳的准确性和响应时间。我们使用四个数据集评估了分布式BDCA系统的QuickAdapt。我们的评估表明,与现有解决方案相比,QuickAdapt平均减少了105倍的适应时间,竞争适应精度为70%。
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