论文标题

解决网络安全数据共享悖论以通过共享数据共享的方法扩展网络安全

Resolving the cybersecurity Data Sharing Paradox to scale up cybersecurity via a co-production approach towards data sharing

论文作者

Atapour-Abarghouei, Amir, McGough, Andrew Stephen, Wall, David Stanley

论文摘要

随着网络犯罪分子扩大其运营以增加其利润或造成更大的伤害,我们认为通过扩大网络安全性来应对威胁的需求相同。为了实现这一目标,我们必须通过克服网络安全数据共享悖论来开发一种共享数据收集的共同生产方法。在这里,我们大家都同意问题和最终目标的定义(改善网络安全并摆脱网络犯罪),但我们不同意如何实现问题和有效地共同努力。这种悖论的核心是,公共利益与私人利益不同。结果,工业和执法部门在寻求以自己的利益来解决事件时采取不同的方法来解决网络安全问题,这表现在两者之间以及其他有关方面之间的不同数据共享实践中,例如网络安全研究人员。我们问的一个大问题是,这些利益是否可以调和,以开发一种跨学科的方法来合作和共享数据。从本质上讲,所有这三个都必须共同存在问题才能共同生产解决方案。我们认为,存在一些具有良好实践的操作模型,这些模型为可能的解决方案提供指南,尤其是多个第三方所有权组织,这些组织合并,匿名和分析数据。为了实现这一目标,我们建议以部门组织联合生产数据收集的实用解决方案,但承认,数据收集的共同标准也将必须制定并达成共识。我们提出了一组最初的最佳实践,用于建立合作和共享数据,并认为需要开发和标准化这些最佳实践,以减轻悖论。

As cybercriminals scale up their operations to increase their profits or inflict greater harm, we argue that there is an equal need to respond to their threats by scaling up cybersecurity. To achieve this goal, we have to develop a co-productive approach towards data collection and sharing by overcoming the cybersecurity data sharing paradox. This is where we all agree on the definition of the problem and end goal (improving cybersecurity and getting rid of cybercrime), but we disagree about how to achieve it and fail to work together efficiently. At the core of this paradox is the observation that public interests differ from private interests. As a result, industry and law enforcement take different approaches to the cybersecurity problem as they seek to resolve incidents in their own interests, which manifests in different data sharing practices between both and also other interested parties, such as cybersecurity researchers. The big question we ask is can these interests be reconciled to develop an interdisciplinary approach towards co-operation and sharing data. In essence, all three will have to co-own the problem in order to co-produce a solution. We argue that a few operational models with good practices exist that provide guides to a possible solution, especially multiple third-party ownership organisations which consolidate, anonymise and analyse data. To take this forward, we suggest the practical solution of organising co-productive data collection on a sectoral basis, but acknowledge that common standards for data collection will also have to be developed and agreed upon. We propose an initial set of best practices for building collaborations and sharing data and argue that these best practices need to be developed and standardised in order to mitigate the paradox.

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