Merging Datasets of CyberSecurity Incidents for Fun and Insight
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, industr...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-01-01
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| Series: | Frontiers in Big Data |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fdata.2020.521132/full |
| _version_ | 1819283190928375808 |
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| author | Giovanni Abbiati Giovanni Abbiati Silvio Ranise Silvio Ranise Antonio Schizzerotto Antonio Schizzerotto Alberto Siena |
| author_facet | Giovanni Abbiati Giovanni Abbiati Silvio Ranise Silvio Ranise Antonio Schizzerotto Antonio Schizzerotto Alberto Siena |
| author_sort | Giovanni Abbiati |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-24T01:27:33Z |
| format | Article |
| id | doaj.art-c58eca96e3a8412895a1d03a8a748e00 |
| institution | Directory Open Access Journal |
| issn | 2624-909X |
| language | English |
| last_indexed | 2024-12-24T01:27:33Z |
| publishDate | 2021-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Big Data |
| spelling | doaj.art-c58eca96e3a8412895a1d03a8a748e002022-12-21T17:22:28ZengFrontiers Media S.A.Frontiers in Big Data2624-909X2021-01-01310.3389/fdata.2020.521132521132Merging Datasets of CyberSecurity Incidents for Fun and InsightGiovanni Abbiati0Giovanni Abbiati1Silvio Ranise2Silvio Ranise3Antonio Schizzerotto4Antonio Schizzerotto5Alberto Siena6Department of Social and Political Sciences, University of Milan, Milan, ItalyFondazione Bruno Kessler, Trento, ItalyFondazione Bruno Kessler, Trento, ItalyDepartment of Mathematics, University of Trento, Trento, ItalyFondazione Bruno Kessler, Trento, ItalyDepartment of Mathematics, University of Trento, Trento, ItalyFondazione Bruno Kessler, Trento, ItalyProviding 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.https://www.frontiersin.org/articles/10.3389/fdata.2020.521132/fullcyber securitydata analysissecurity incidents statisticsmethodological frameworkdata breaches |
| spellingShingle | Giovanni Abbiati Giovanni Abbiati Silvio Ranise Silvio Ranise Antonio Schizzerotto Antonio Schizzerotto Alberto Siena Merging Datasets of CyberSecurity Incidents for Fun and Insight Frontiers in Big Data cyber security data analysis security incidents statistics methodological framework data breaches |
| title | Merging Datasets of CyberSecurity Incidents for Fun and Insight |
| title_full | Merging Datasets of CyberSecurity Incidents for Fun and Insight |
| title_fullStr | Merging Datasets of CyberSecurity Incidents for Fun and Insight |
| title_full_unstemmed | Merging Datasets of CyberSecurity Incidents for Fun and Insight |
| title_short | Merging Datasets of CyberSecurity Incidents for Fun and Insight |
| title_sort | merging datasets of cybersecurity incidents for fun and insight |
| topic | cyber security data analysis security incidents statistics methodological framework data breaches |
| url | https://www.frontiersin.org/articles/10.3389/fdata.2020.521132/full |
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