Predictive Insights Into Healthcare Data Breaches: A Time Series Forecasting Approach

Abstract

The healthcare industry is increasingly becoming a prime target for cyberattacks, with data breaches leading to significant financial losses, regulatory penalties, and compromised patient privacy. Traditional methods of breach detection are largely reactive, often identifying breaches only after they have occurred, leaving healthcare organizations vulnerable. In this research, we propose a proactive approach by applying time series forecasting techniques to predict healthcare data breaches, providing actionable insights that can help mitigate future risks.
This study employs historical data on healthcare data breaches and applies various time series models, including AutoRegressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), and Long Short-Term Memory (LSTM) networks, to analyze and forecast breach trends. Through rigorous preprocessing and analysis, we address key time series components such as trend, seasonality, and cyclicality. Our models are evaluated using standard metrics such as Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) to ensure accuracy and reliability.
The results of this study reveal critical patterns in breach occurrences, including seasonal peaks and long-term trends that highlight vulnerable periods for healthcare organizations. The most effective forecasting model is identified, offering high predictive accuracy and allowing stakeholders to anticipate and respond to breach risks more effectively. These findings suggest that time series forecasting can serve as a valuable tool for healthcare providers and cybersecurity professionals to enhance their data security strategies.
By forecasting potential breach events, this research not only contributes to the growing body of work on predictive cybersecurity but also provides practical insights for improving healthcare data protection. Future work may involve integrating advanced machine learning models to further refine breach predictions and expanding the approach to other sectors facing cybersecurity challenges.