ArticleNetwork modeling and analysis in health informatics and bioinformatics2022
Using attack graphs to defend healthcare systems from cyberattacks: a longitudinal empirical study.
Article in Network modeling and analysis in health informatics and bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Mapping Machine Learning-Driven Cybersecurity Solutions in Health Care: Scoping Literature Review.Journal of medical Internet research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cyber security encompasses a variety of financial, political, and social aspects with significant implications for the safety of individuals and organisations. Hospitals are among the least secure and most vulnerable organisations in terms of cybersecurity. Protecting medical records from cyberattacks is critical for protecting personal and financial records of those involved in medical institutions. Attack graphs, like in other systems, can be used to protect medical and hospital records from cyberattacks. In the current study, a total of 352 real-life cyberattacks on healthcare institutions using common vulnerability scoring system (CVSS) data were statistically examined to determine important trends and specifications in regard to those attacks. Following that, several machine learning techniques and an artificial neural network model were used to model industrial control systems (ICS) vulnerability data of those attacks. The average vulnerability score for attacks on healthcare IT systems was found to be very high. Moreover, this score was found to be higher in healthcare institutions which have experienced cyberattacks in the past and no mitigation actions were implemented. Using Python programming software, the most successful model that can be used in modelling cyberattacks on IT systems of healthcare institutions was found to be the
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.