📌 Context of the Dataset

The Healthcare Ransomware Dataset was created to simulate real-world cyberattacks in the healthcare industry. Hospitals, clinics, and research labs have become prime targets for ransomware due to their reliance on real-time patient data and legacy IT infrastructure. This dataset provides insight into attack patterns, recovery times, and cybersecurity practices across different healthcare organizations.

Why is this important?

Ransomware attacks on healthcare organizations can shut down entire hospitals, delay treatments, and put lives at risk. Understanding how different healthcare organizations respond to attacks can help develop better security strategies. The dataset allows cybersecurity analysts, data scientists, and researchers to study patterns in ransomware incidents and explore predictive modeling for risk mitigation.

📌 Sources and Research Inspiration This simulated dataset was inspired by real-world cybersecurity reports and built using insights from official sources, including:

1️⃣ IBM Cost of a Data Breach Report (2024)

The healthcare sector had the highest average cost of data breaches ($10.93 million per incident). On average, organizations recovered only 64.8% of their data after paying ransom. Healthcare breaches took 277 days on average to detect and contain.

2️⃣ Sophos State of Ransomware in Healthcare (2024)

67% of healthcare organizations were hit by ransomware in 2024, an increase from 60% in 2023. 66% of backup compromise attempts succeeded, making data recovery significantly more difficult. The most common attack vectors included exploited vulnerabilities (34%) and compromised credentials (34%).

3️⃣ Health & Human Services (HHS) Cybersecurity Reports

Ransomware incidents in healthcare have doubled since 2016. Organizations that fail to monitor threats frequently experience higher infection rates.

4️⃣ Cybersecurity & Infrastructure Security Agency (CISA) Alerts

Identified phishing, unpatched software, and exposed RDP ports as top ransomware entry points. Only 13% of healthcare organizations monitor cyber threats more than once per day, increasing the risk of undetected attacks.

5️⃣ Emsisoft 2020 Report on Ransomware in Healthcare

The number of ransomware attacks in healthcare increased by 278% between 2018 and 2023. 560 healthcare facilities were affected in a single year, disrupting patient care and emergency services.

📌 Why is This a Simulated Dataset?

This dataset does not contain real patient data or actual ransomware cases. Instead, it was built using probabilistic modeling and structured randomness based on industry benchmarks and cybersecurity reports.

How It Was Created:

1️⃣ Defining the Dataset Structure

The dataset was designed to simulate realistic attack patterns in healthcare, using actual ransomware case studies as inspiration.

Columns were selected based on what real-world cybersecurity teams track, such as: Attack methods (phishing, RDP exploits, credential theft). Infection rates, recovery time, and backup compromise rates. Organization type (hospitals, clinics, research labs) and monitoring frequency.

2️⃣ Generating Realistic Data Using ChatGPT & Python

ChatGPT assisted in defining relationships between attack factors, ensuring that key cybersecurity concepts were accurately reflected. Python’s NumPy and Pandas libraries were used to introduce randomized attack simulations based on real-world statistics. Data was validated against industry research to ensure it aligns with actual ransomware attack trends.

3️⃣ Ensuring Logical Relationships Between Data Points

Hospitals take longer to recover due to larger infrastructure and compliance requirements. Organizations that track more cyber threats recover faster because they detect attacks earlier. Backup security significantly impacts recovery time, reflecting the real-world risk of backup encryption attacks.

In 2024, the number of data compromises in the United States stood at 3,158 cases. Meanwhile, over 1.35 billion individuals were affected in the same year by data compromises, including data breaches, leakage, and exposure. While these are three different events, they have one thing in common. As a result of all three incidents, the sensitive data is accessed by an unauthorized threat actor. Industries most vulnerable to data breaches Some industry sectors usually see more significant cases of private data violations than others. This is determined by the type and volume of the personal information organizations of these sectors store. In 2024 the financial services, healthcare, and professional services were the three industry sectors that recorded most data breaches. Overall, the number of healthcare data breaches in some industry sectors in the United States has gradually increased within the past few years. However, some sectors saw decrease. Largest data exposures worldwide In 2020, an adult streaming website, CAM4, experienced a leakage of nearly 11 billion records. This, by far, is the most extensive reported data leakage. This case, though, is unique because cyber security researchers found the vulnerability before the cyber criminals. The second-largest data breach is the Yahoo data breach, dating back to 2013. The company first reported about one billion exposed records, then later, in 2017, came up with an updated number of leaked records, which was three billion. In March 2018, the third biggest data breach happened, involving India’s national identification database Aadhaar. As a result of this incident, over 1.1 billion records were exposed.

Climate change is expected to alter the distributions and community composition of stream fishes in the Great Lakes region in the 21st century, in part as a result of altered hydrological systems (stream temperature, streamflow, and habitat). Resource managers need information and tools to understand where fish species and stream habitats are expected to change under future conditions. Fish sample collections and environmental variables from multiple sources across the United States Great Lakes Basin were integrated and used to develop empirical models to predict fish species occurrence under present-day climate conditions. Random Forests models were used to predict the probability of occurrence of 13 lotic fish species within each stream reach in the study area. Downscaled climate data from general circulation models were integrated with the fish species occurrence models to project fish species occurrence under future climate conditions. The 13 fish species represented three ecological guilds associated with water temperature (cold, cool, and warm), and the species were distributed in streams across the Great Lakes region. Vulnerability (loss of species) and opportunity (gain of species) scores were calculated for all stream reaches by evaluating changes in fish species occurrence from present-day to future climate conditions. The 13 fish species included 4 cold-water species, 5 cool-water species, and 4 warm-water species. Presently, the 4 cold-water species occupy from 15 percent (55,000 kilometers [km]) to 35 percent (130,000 km) of the total stream length (369,215 km) across the study area; the 5 cool-water species, from 9 percent (33,000 km) to 58 percent (215,000 km); and the 4 warm-water species, from 9 percent (33,000 km) to 38 percent (141,000 km). Fish models linked to projections from 13 downscaled climate models projected that in the mid to late 21st century (2046–65 and 2081–2100, respectively) habitats suitable for all 4 cold-water species and 4 of 5 cool-water species under present-day conditions will decline as much as 86 percent and as little as 33 percent, and habitats suitable for all 4 warm-water species will increase as much as 33 percent and as little as 7 percent. This report documents the approach and data used to predict and project fish species occurrence under present-day and future climate conditions for 13 lotic fish species in the United States Great Lakes Basin. A Web-based decision support mapping application termed “FishVis” was developed to provide a means to integrate, visualize, query, and download the results of these projected climate-driven responses and help inform conservation planning efforts within the region. A geodatabase containing the full dataset of results that are being mapped in FishVis can be downloaded from the FishVis mapping application at http://ccviewer.wim.usgs.gov/FishVis/ or through USGS ScienceBase as a Data Release (Stewart and others, 2016). The geodatabase contains five feature classes, each with their own metadata record and include data attributed to the stream reach (fishvis_reacha83 and fishvis_search_reacha83), catchment (fishvis_catcha83 and fishvis_reacha83), and huc12 (fishvis_huc12a83). The citation for the USGS Scientific Investigation Report that documents this dataset is: Stewart, J.S., Covert, S.A., Estes, N.J., Westenbroek, S.M., Krueger, Damon, Wieferich, D.J., Slattery, M.T., Lyons, J.D., McKenna, J.E., Jr., Infante, D.M., Bruce, J.L., 2016, FishVis, A regional decision support tool for identifying vulnerabilities of riverine habitat and fishes to climate change in the Great Lakes Region: U.S. Geological Survey Scientific Investigations Report 2016-5124, 15 p., http://dx.doi.org/10.3133/sir20165124.

During the third quarter of 2024, data breaches exposed more than *** million records worldwide. Since the first quarter of 2020, the highest number of data records were exposed in the first quarter of ***, more than *** million data sets. Data breaches remain among the biggest concerns of company leaders worldwide. The most common causes of sensitive information loss were operating system vulnerabilities on endpoint devices. Which industries see the most data breaches? Meanwhile, certain conditions make some industry sectors more prone to data breaches than others. According to the latest observations, the public administration experienced the highest number of data breaches between 2021 and 2022. The industry saw *** reported data breach incidents with confirmed data loss. The second were financial institutions, with *** data breach cases, followed by healthcare providers. Data breach cost Data breach incidents have various consequences, the most common impact being financial losses and business disruptions. As of 2023, the average data breach cost across businesses worldwide was **** million U.S. dollars. Meanwhile, a leaked data record cost about *** U.S. dollars. The United States saw the highest average breach cost globally, at **** million U.S. dollars.

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A heatwave refers to a prolonged period of unusually hot weather. While there is no standard definition of a heatwave in England, the Met Office generally uses the World Meteorological Organization definition of a heatwave, which is "when the daily maximum temperature of more than five consecutive days exceeds the average maximum temperature by 5°C, the normal period being 1961-1990". They are common in the northern and southern hemisphere during summer, and have historically been associated with health problems and an increase in mortality. The urban heat island (UHI) is the phenomenon where temperatures are relatively higher in cities compared to surrounding rural areas due to, for example, the urban surfaces and anthropogenic heat sources. For an example of an urban heat island map during an average summer, see this dataset. For an example of an urban heat island map during a warm summer, see this dataset. As well as outdoor temperature, an individual’s heat exposure may also depend on the type of building they are inside, if indoors. Indoor temperature exposure may depend on a number of characteristics, such as the building geometry, construction materials, window sizes, and the ability to add extra ventilation. It is also known that people have different vulnerabilities to heat, with some more prone to negative health issues when exposed to high temperatures. This Triple Jeopardy dataset combines: Urban Heat Island information for London, based on the 55 days between May 26th -July 19th 2006, where the last four days were considered a heatwave An estimate of the indoor temperatures for individual dwellings in London across this time period Population age, as a proxy for heat vulnerability, and distribution From this, local levels of heat-related mortality were estimated using a mortality model derived from epidemiological data. The dataset comprises four layers: Ind_Temp_A – indoor Temperature Anomaly is the difference in degrees Celsius between the estimated indoor temperatures for dwellings and the average indoor temperature estimate for the whole of London, averaged by ward. Positive numbers show dwellings with a greater tendency to overheat in comparison with the London average HeatMortpM – total estimated mortality due to heat (outdoor and indoor) per million population over the entire 55 day period, inclusive of age effects HeatMorUHI – estimated mortality per million population due to increased outdoor temperature exposure caused by the UHI over the 55 day period (excluding the effect of overheating housing), inclusive of age effects HeatMorInd - estimated mortality per million population due to increased temperature exposure caused by heat-vulnerable dwellings (excluding the effect of the UHI) over the 55 day period, inclusive of age effects More information is on this website and in the Triple Jeopardy leaflet. The maps are also available as one combined PDF. More information is on this website and in the Triple Jeopardy leaflet.

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