Mikaëla Ngamboé

Mikaëla Ngamboé, Jean-Simon Marrocco, Jean-Yves Ouattara et al.

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.

Ngamboé Mikaela, Berthier Paul, Ammari Nader et al.

Cardiac Implantable Electronic Devices (CIED) are fast becoming a fundamental tool of advanced medical technology and a key instrument in saving lives. Despite their importance, previous studies have shown that CIED are not completely secure against cyber attacks and especially those who are exploiting their Radio Frequency (RF) communication interfaces. Furthermore, the telemetry capabilities and IP connectivity of the external devices interacting with the CIED are creating other entry points that may be used by attackers. In this paper, we carry out a realistic risk analysis of such attacks. This analysis is composed of three parts. First, an actor-based analysis to determine the impact of the attacks. Second, a scenario-based analysis to determine the probability of occurrence of each threat. Finally, a combined analysis to determine which attack outcomes (i.e. attack goals) are riskiest and to identify the vulnerabilities that constitute the highest overall risk exposure. The conducted study showed that the vulnerabilities associated with the RF interface of CIED represent an acceptable risk. In contrast, the network and internet connectivity of external devices represent an important potential risk. The previously described findings suggest that the highest risk is associated with external systems and not the CIED itself.