Research on Dataset Construction and Risk Analysis for Cross-Domain Attacks in Industrial Control Scenarios

As the security threats to industrial control systems continue to increase, understanding and defending against cross-domain attacks has become particularly important. This study focuses on cross-domain attacks in industrial control environments and proposes a new framework for dataset construction and risk analysis. Through meticulous data collection and processing mechanisms specific to industrial control, we have designed a dataset with features and structures that are suitable for industrial control scenarios. This dataset provides an empirical foundation for assessing attack patterns and impacts, allowing for an in-depth risk quantification analysis. Furthermore, we have developed a series of risk assessment models targeting cross-domain attacks. The findings of this study not only expand the research scope of datasets in the field of industrial control but also provide theoretical and empirical support for achieving more accurate risk identification and protection against cross-domain attacks, demonstrating a certain degree of innovation and practical value.