The DOI matches its Crossref record; 158 of the 158 references it cites were found in a registry or an index.
Cyber risk and cybersecurity: a systematic review of data availability
Open accessAbstract
Cybercrime is estimated to have cost the global economy just under USD 1 trillion in 2020, indicating an increase of more than 50% since 2018. With the average cyber insurance claim rising from USD 145,000 in 2019 to USD 359,000 in 2020, there is a growing necessity for better cyber information sources, standardised databases, mandatory reporting and public awareness. This research analyses the extant academic and industry literature on cybersecurity and cyber risk management with a particular focus on data availability. From a preliminary search resulting in 5219 cyber peer-reviewed studies, the application of the systematic methodology resulted in 79 unique datasets. We posit that the lack of available data on cyber risk poses a serious problem for stakeholders seeking to tackle this issue. In particular, we identify a lacuna in open databases that undermine collective endeavours to better manage this set of risks. The resulting data evaluation and categorisation will support cybersecurity researchers and the insurance industry in their efforts to comprehend, metricise and manage cyber risks.
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Cites
1 retracted
Deng, L., Li, D., Yao, X., & Wang, H. (2018) · Cluster Computing · doi:10.1007/s10586-018-1847-2 · Cited by 124
RetractedThe PRISMA 2020 statement: an updated guideline for reporting systematic reviews
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., et al. (2021) · Systematic Reviews · doi:10.1186/s13643-021-01626-4 · Cited by 13,874
Survey of intrusion detection systems: Techniques, datasets and challenges
Khraisat, A., Gondal, I., Vamplew, P., & Kamruzzaman, J. (2019) · Cybersecurity · doi:10.1186/s42400-019-0038-7 · Cited by 2,024
Deep Learning Approach for Intelligent Intrusion Detection System
Vinayakumar, R., Alazab, M., Soman, K. P., Poornachandran, P., Al-Nemrat, A., & Venkatraman, S. (2019) · IEEE Access · doi:10.1109/access.2019.2895334 · Cited by 1,946
A survey of network anomaly detection techniques
Ahmed, M., Naser Mahmood, A., & Hu, J. (2016) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2015.11.016 · Cited by 1,750
Machine Learning and Deep Learning Methods for Cybersecurity
Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., Gao, M., Hou, H., & Wang, C. (2018) · IEEE Access · doi:10.1109/access.2018.2836950 · Cited by 1,228
Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey
Liu, H., & Lang, B. (2019) · Applied Sciences · doi:10.3390/app9204396 · Cited by 1,133
Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study
Ferrag, M. A., Maglaras, L., Moschoyiannis, S., & Janicke, H. (2020) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2019.102419 · Cited by 1,130
Alsaedi, A., Moustafa, N., Tari, Z., Mahmood, A., & Anwar, A. (2020) · IEEE Access · doi:10.1109/access.2020.3022862 · Cited by 892
Cybersecurity data science: an overview from machine learning perspective
Sarker, I. H., Kayes, A. S. M., Badsha, S., Alqahtani, H., Watters, P., & Ng, A. (2020) · Journal of Big Data · doi:10.1186/s40537-020-00318-5 · Cited by 766
Cited by
Nankya, M., Chataut, R., & Akl, R. (2023) · Sensors · doi:10.3390/s23218840 · Cited by 114
Mohamed, A. S., Fritz, C., Saber, A. M., Ma, Y., Kaur, R., Al-Darwbi, M., Friedrich, D., & Kundur, D. (2026) · arXiv · doi:10.48550/arxiv.2609.00171
“For example, organizations that experience cyber incidents rarely disclose them publicly, due to concerns over reputation damage, loss of goodwill, or brand impact [99], [103], [104].”
Metadata
- Authors
- Cremer, F., Sheehan, B., Fortmann, M., Kia, A. N., Mullins, M., Murphy, F., & Materne, S.
- Venue
- The Geneva Papers on Risk and Insurance - Issues and Practice
- Published
- 2022-02-17
- Volume
- 47
- Issue
- 3
- Pages
- 698-736
- ISSN
- 1018-5895 (ISSN-L)
- 1468-0440
- OpenAlex
- W4213292645
- Cited by
- 355
- References
- 158
- FWCI
- 10.47
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Cremer, F., Sheehan, B., Fortmann, M., Kia, A. N., Mullins, M., Murphy, F., & Materne, S. (2022). Cyber risk and cybersecurity: a systematic review of data availability. The Geneva Papers on Risk and Insurance - Issues and Practice. https://doi.org/10.1057/s41288-022-00266-6
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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