The aeronautical communications systems are susceptible to temporary or permanent disruptions. These can be caused by local conditions, such as a high number of aircraft trying to send or receive data with ground operators, a lack of coverage between aircraft and ground centres or weather conditions degrading satellite communications (SATCOM). These systems are also vulnerable to cyberattacks, a growing concern within the aeronautical industry.
• Up until now, over the current ATN, it takes six minutes for a datalink disruption to be confirmed, a period during which controllers’ orders cannot be executed by pilots. SINAPSE assessed monitoring solution that uses artificial intelligence to predict such outages and that can be integrated into future aeronautical systems with prevention capabilities. Any disruption experienced by one aircraft has indirect implications for other aircraft in the vicinity, as airspace capacity is degraded, and planes may be delayed or re-routed. Any anticipation or prediction of these outages helps to reduce their impact on overall traffic. SINAPSE implemented a real-time operational data and network monitoring to predict communication failures using Controller Pilot Data Link Communications (CPDLC) data, captured in real-time from the operational ATN. A targeted use case demonstrated that SINAPSE could continuously predict and forecast disruption events ten minutes before they happened. This information could be very useful and could eventually prevent communication loss events in different ways.
• SINAPSE proposed an SDN design with a distributed software architecture that allows for increased configuration over the network, with everything monitored by a central controller layer. In traditional systems, this controller-like concept relies mainly on humans and is not suitable for automation, but SINAPSE introduced artificial intelligence as the controller, to manage the system more efficiently. The AI automatically checks for faults in the networks and using predictive information can proactively adjust the system and perform maintenance. SINAPSE consolidated the design of a Multi-layered hybrid hierarchical control plan structure that reduces the complexity growth of the SDN by partitioning the network into multiple segments and assigning several controllers to each to improve the scalability.
• An assessment of Machine Learning (ML) methodologies was also applied to predict the probability of transmission errors over satellite link communication that provides interactive voice and data telecommunication services for air traffic control and, as such, requires high availability and performance. Outages do not only result in a degradation of service, but they also constitute safety risks. The idea is to use these predictions to perform network optimizations. The ML model consisted of predicting the signal strength of a satellite link. The satellite link performance data was combined with associated regional weather data to create training dataset.
• SINAPSE Studied safety filter concept for data prediction responsible for deciding on the usability of the ML model predicted data. Safety filter works as a safeguard, without human intervention, and qualifies the predicted sensor data, as valid or invalid, by applying captured expertise rules. This concept contributes to making AI safer and keeps it in check.
• SINAPSE studied ML algorithms, in a federated machine learning architecture, to analyse the network traffic for signatures known to match cyberattacks. These models directly feed the IPDS as part of a defence-in-depth approach to protect the network against malicious traffic. The federated learning (FL) network architecture ensures that only the AI’s models are shared among users, without the need for underlying data to be shared—enhancing security further. This type of collaborative cybersecurity function will be a crucial building block for a secured future aeronautical communication infrastructure.
