Résumé
Swarms of drones are more and more used for critical missions and need to be protected against malicious users. Intrusion Detection Systems (IDS) are used to analyze network traffic in order to detect possible threats. Modern IDSs rely on machine learning models for such a sake. Because of the absence of central management in swarms of drones, IDSs constitute a good second-line protective measure. Investigating the execution of IDS (resource-hungry) algorithms on drone (resource-constrained) devices is crucial when it comes to optimizing energy, response time, memory footprint and algorithm precision. In addition, embedded platforms used in drones often incorporate heterogeneous computing platforms on which IDSs could be executed. In this paper, we present a methodology and results about characterizing the execution of different IDS models on various platform (CPUs, GPUs). In effect, as swarm of drones operate in different mission contexts (e.g. criticity level) and states (e.g. energy budget, memory footprint), it is important to explore which IDS model to run on which platforms for a given mission in a given context. For this sake, we evaluated several metrics on different platforms: energy and resource consumption, accuracy for malicious traffic detection and response time. The models tested (RF, CNN, DNN) have shown different performance according to the measured metrics and the chosen platform and proved to be relevant in different mission states.