Use Cases

I - Improvement of flash flood forecasting thanks to the use of AI – CS

The SPACE Business Unit of CS GROUP brings its technical expertise to design & develop the use case “Improvement of flash flood forecasting thanks to the use of AI”. This use case is about testing the feasibility of modelling flood events by neural network, and to evaluate the sensitivity of the model to the available data. 

For this purpose, we propose the following 4-step approach: 

  1. Establish a resulting fine-scale hydrodynamic reference model on the selected area with historical episodes.  
  2. Develop and adjust the AI model with a minimum number of parameters, easily accessible.   
  3. Qualify the AI model by comparison with theresults of the hydrodynamic model, considered as the reference to be reached.  
  4. Qualify the AI model by testing the impact and sensitivity to input parameters, weights, etc. to avoid over-sampling, and/or to avoid missing some key parameters.   

For its top-notch representativeness of flash flood events in urban areas, the city of Nîmes was chosen. The existence of the ESPADA system, the network of sensors in place, the richness of historical data makes it an excellent ground for the development of the model. 

If the results are up to expectations, the methodology could be applied to other urbanized areas, not benefiting from these input data. 

It is also expected that the CS METIS platform will be used for the implementation and configuration of the hydrodynamic reference models as well as the integration of the AI models in order to optimize them by reducing the input parameters, the number of announcement types, while taking advantage of the ExtremeXP environment (testing and validation of the use case on the external platform that will be provided by a project partner).

II - Increased Cybersecurity situation awareness for efficient threat mitigation i2CAT/UPC/ITU

Attackers evolve perpetually by utilizing advanced techniques and refining of implements and tactics for performing targeted penetration strategies directed towards internal and external access points of organizations. The main challenge exists in having reliable and robust detection i.e., high accuracy and low false positives. It is necessary to develop methods that can utilize the correlation between suspicious information flows that arise during an attacker campaign and break their kill chain. The main challenge remains is the semantic gap between the low-level alerts and high-level kill chain view of the attacker’s behaviour. In our analysis, we aim to bridge the semantic gap with the behavioural patterns in MITRE ATT&CK framework such that we can effectively match the low-level alerts to the TTPs, discover the correlation between the attack steps and reduce the false positives. In this use case, we propose an early multistage real-time attack detection with an awareness of adversary Tactics, Techniques, and Procedures (TTP) as suggested in the below architecture.

III - Situational intelligence and decision making for PPDR (Public Protection and Disaster Relief) – ADS

Description will be available soon

IV - Flexible transportation analysis and visualization – MOBY

Description will be available soon

V - Failure prevention for manufacturing industry – IDK

Maximizing machine uptime in an industrial plant is crucial for attaining high overall system availability and maintaining competitiveness. The Industry 4.0 paradigm has introduced novel techniques and methods to achieve this objective, including predictive maintenance, remaining lifetime assessment, and critical machine element diagnosis.  

The key is to have precise information and avoid erroneous warnings that lead to a decrease of the production capacity. With this in mind, the objective is to have a dashboard for the technical service team that allows them to take decisions over incipient and imminent failures of the critical components of the machine, such as heads, balls screws or axis.

The ExtremeXP project is co-funded by the European Union Horizon Program HORIZON-CL4-2022-DATA-01-01, under Grant Agreement No. 101093164
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