Frontiers in Big Data (Jan 2021)
Merging Datasets of CyberSecurity Incidents for Fun and Insight
Abstract
Providing an adequate assessment of their cyber-security posture requires companies and organisations to collect information about threats from a wide range of sources. One of such sources is history, intended as the knowledge about past cyber-security incidents, their size, type of attacks, industry sector and so on. Ideally, having a large enough dataset of past security incidents, it would be possible to analyze it with automated tools and draw conclusions that may help in preventing future incidents. Unfortunately, it seems that there are only a few publicly available datasets of this kind that are of good quality. The paper reports our initial efforts in collecting all publicly available security incidents datasets, and building a single, large dataset that can be used to draw statistically significant observations. In order to argue about its statistical quality, we analyze the resulting combined dataset against the original ones. Additionally, we perform an analysis of the combined dataset and compare our results with the existing literature. Finally, we present our findings, discuss the limitations of the proposed approach, and point out interesting research directions.
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