Abstract
With the continuous advancement of the informationization of air traffic control (ATC) in civil aviation, the ATC system currently acts as a hub supporting the efficient and safe operation of the aviation transportation industry. In this process, a large volume of business data is generated and processed within the ATC system that needs to be exchanged across different domains with external entities or organizations to meet the growing demands of informatization. However, data security, real-time processing, and efficiency issues have become increasingly prominent, posing bottlenecks to the further development of the ATC system. Driven by the promotion of informationization of the ATC system, the application subsystems within the civil aviation ATC system have gradually become fragmented, forming multiple information silos. This not only hinders the effective circulation of information but also limits the overall operational efficiency of the ATC system. Therefore, facilitating information sharing and system integration has become a critical task in the current phase of informationization. The exchange of information across industries, business domains, and organizations is a key aspect of achieving these goals. The process of cross-domain information exchange is considerably more complex than simply transmitting information from one place to another, involving multiple stages such as information storage, metadata registration, user identity authentication, and access control. Moreover, cross-domain information exchange also faces many challenges, including data heterogeneity, platform heterogeneity, distribution, autonomy, and security. This study aims to address these challenges by proposing a model for the automatic classification of multisource heterogeneous ATC operational data security to enhance data management, ensure security, promote information sharing, and facilitate business collaboration within the civil aviation ATC system.
Herein, first, a dataset is constructed to facilitate the classification of the ATC operational data security. Representative data from various operational categories are selected, and 13 key security attributes are identified to design the data security classification. Five security levels are established based on relevant laws and regulations pertaining to data security and the characteristics of the civil aviation ATC operational data. Subsequently, an automatic classification model is developed based on the AdaBoost algorithm with the classification and regression tree (CART) as the base classifier, considering the unique characteristics of the ATC operational data.
Experimental results demonstrate the effectiveness of the proposed automatic classification model. A comparative analysis of the proposed model against other machine learning algorithms reveals that the proposed model achieves the highest accuracy rate, reaching 95.5%. Thus, the proposed model successfully classifies multisource heterogeneous ATC operational data according to their security attributes, enabling the formulation of tailored security strategies and access control mechanisms for different data security levels.
This proposed model considerably enhances the data management capabilities of the civil aviation ATC system, ensures data security, promotes information sharing, and facilitates business collaboration within the system. Thus, this study provides a robust framework for addressing the challenges associated with data security and integration in complex operational environments, laying a foundation for further advancements in civil aviation ATC informationization.
