New Machine Learning Approaches for Intrusion Detection in ADS-B

• URL: http://arxiv.org/abs/2510.08333v1
• Date: Thu, 09 Oct 2025 15:22:20 GMT
• Title: New Machine Learning Approaches for Intrusion Detection in ADS-B
• Authors: Mikaëla Ngamboé, Jean-Simon Marrocco, Jean-Yves Ouattara, José M. Fernandez, Gabriela Nicolescu,
• Abstract summary: Air traffic management relies on the vulnerable Automatic Dependent Surveillance-Broadcast (ADS-B) protocol.<n>This study investigates emerging machine learning models and training strategies to improve AI-based intrusion detection systems (IDS) for ADS-B.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the growing reliance on the vulnerable Automatic Dependent Surveillance-Broadcast (ADS-B) protocol in air traffic management (ATM), ensuring security is critical. This study investigates emerging machine learning models and training strategies to improve AI-based intrusion detection systems (IDS) for ADS-B. Focusing on ground-based ATM systems, we evaluate two deep learning IDS implementations: one using a transformer encoder and the other an extended Long Short-Term Memory (xLSTM) network, marking the first xLSTM-based IDS for ADS-B. A transfer learning strategy was employed, involving pre-training on benign ADS-B messages and fine-tuning with labeled data containing instances of tampered messages. Results show this approach outperforms existing methods, particularly in identifying subtle attacks that progressively undermine situational awareness. The xLSTM-based IDS achieves an F1-score of 98.9%, surpassing the transformer-based model at 94.3%. Tests on unseen attacks validated the generalization ability of the xLSTM model. Inference latency analysis shows that the 7.26-second delay introduced by the xLSTM-based IDS fits within the Secondary Surveillance Radar (SSR) refresh interval (5-12 s), although it may be restrictive for time-critical operations. While the transformer-based IDS achieves a 2.1-second latency, it does so at the cost of lower detection performance.

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