Exploring the Variations Among ARIMA Models for Time Series Forecasting of Data Breaches

Data breaches are widely reported in the media, attracting the attention of dedicated scientists and professionals working on solutions. Commercial organizations, businesses, and government agencies that acquire, handle, and retain personal or business-related data face the risk of client personal information and organizational intellectual property being compromised, resulting in potential legal, reputational, and financial harm. Data breaches represent a critical cybersecurity challenge that has led to financial losses and infringements of privacy, including the compromise of social security numbers. This underscores the necessity for a more profound understanding of the risks associated with data breaches. Despite much focus, some fundamental issues persist unaddressed. This study concentrates on the modeling and prediction of data breaches through time series forecasting algorithms. The forecasting techniques for time series data represent an emerging area of research, driven by the increasing complexity of such data. This paper analyzes modern modeling methodologies, compares different approaches, and outlines potential options for time series forecasting. This study aims to leverage ARIMA and its variations, such as SARIMA, for building robust prediction models using historical data to forecast the likelihood and magnitude of future data breaches. A comprehensive dataset, sourced from the Privacy Rights Clearinghouse (PRC), encompassing all documented instances of data breaches in the United States, was utilized as input for the predictive models. The ARIMA and SARIMA models demonstrated strong predictive capabilities, with minimal deviation from actual occurrences, highlighting their potential in accurately forecasting data breach incidences. These findings provide valuable insights for organizations aiming to enhance their cybersecurity strategies through data-driven forecasting approaches.