We propose integrating data visualization and machine learning techniques to support Air Traffic Controller (ATC) systems for detecting and identifying friendly/unfriendly aircraft. Our platform composes data-driven decisions to optimize strategic, and operative elements of an ATC system and mitigates its drawbacks by analyzing real-time data from a radar system. A threat assessment approach that incorporates flight behavior assessment, based on visualizing flight data together with flight anomaly prediction and flight origin/destination prediction can be used in cases where the system fails. Furthermore, our proposed integrated tool lets users quickly identify flights and security concerns by analyzing and visualizing data.