IDS-DEEP: a strategy for selecting the best IDS for Drones with heterogeneous EmbEdded Platforms

Drone swarms are increasingly being used to perform critical missions, such as inspection of ports and industrial installations. Each drone can embed heterogeneous execution platforms to successfully perform various computing tasks. As security threats may disrupt the progression of the drone mission, network intrusion detection systems (IDSs) are used. They analyze network traffic to detect malicious behaviors, but generally rely on resource-hungry machine learning models. To adapt to the dynamic nature of the mission, it is necessary to embed several IDS implementations leveraging heterogeneous computing resources of the drone and presenting a trade-off between security, throughput, and energy consumption. To address this issue, we propose, in this paper, an end-to-end flow composed of an offline phase to choose the IDS implementations to embed on the drone platform and an online phase to select the best implementation online considering the mission conditions at a given time. We devised a MILP formulation for the offline phase that proved to provide a 89.41% better Inverted Generational Distance (IGD) than a random choice. For the online phase, we investigated several solutions and designed a novel optimized strategy that proved to be around 16.76 times faster than TOPSIS while having comparable QoS metrics.