Enhancing aviation control security through ADS-B injection detection using ensemble meta-learning models with Explainable AI

Abstract

The increasing use of Automatic Dependent Surveillance-Broadcast (ADS-B) technology in flight control systems has created many serious concerns.These weaknesses threaten the security and safety of our aviation industry. Therefore, to enhance aviation control security and better deal with these problems, this research focuses on developing a strong ADS-B injection detection system. It combines XGBoost and Random Forest with Logistic Regression in an Ensemble Learning Meta-Learning Model to identify ADS-B injection risks and categorise them. Ensemble methods, which combine several models can increase the detection accuracy and robustness of the model used to identify the threat. In addition, Explainable AI (XAI) methods are employed to enhance the process of explaining how the model reaches its decisions and building trust in aviation security systems. The system’s training, testing, and evaluation are conducted with ADS-B data. This result indicates that the Stacked Random Forest and XGBoost with Logistic Regression Meta-Learner with 99.60% accuracy, along with good recall rates (99.49%) and precision (99.41%). Also, aviation control authorities are reassured by the model’s transparent and applicable decision logic through the application of XAI techniques. This research contributes to enhanced aviation security by proposing a new, highly accurate ADS-B injection detection system with explainable outcomes. A strategy like this can help flight control systems maintain integrity amidst an ever-digitising aviation reality.