Merging Datasets of CyberSecurity Incidents for Fun and Insight - Abbiati, Giovanni; Ranise, Silvio; Schizzerotto, Antonio; Siena, Alberto (Frontiers in Big Data, 2021)
Cyber security incidents can have dramatic economic, social and institutional impact. Providing an adequate assessment of the cyber-security posture for companies and organisations needs to collects 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 analyse it with artificial intelligence 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 analyse the resulting combined dataset against the source ones. Additionally, we perform an analysis of the combined dataset, and compare our results with the existing literature. Finally, we discuss our findings, and discuss the limitations of the proposed approach and point out interesting research directions.