IDS-DEEP2: Strategies for selecting the best IDS for Drones with heterogeneous or homogeneous EmbEdded parallel platforms

Nowadays, swarms of drones are gaining a lot of interest for critical missions like search and rescue. They can be composed of several types of drones, with potentially different processing capabilities to successfully perform various computing tasks. In particular, some may use heterogeneous computing platforms, while others may rely on homogeneous parallel ones. Wireless communications used in the drone swarm are difficult to secure, due to their broadcasting properties and distributed aspects. In addition to cryptographic countermeasures, network Intrusion Detection Systems (IDSs) can act as a second layer of security to detect threats. To do so, they analyze network traffic to detect malicious behaviors, using resource-hungry Machine Learning (ML) models. Due to changing conditions during the swarm mission, it is necessary to embed several IDS implementations to adapt to these changes. This paper proposes two contributions. The first is IDS-DEEP, a strategy to select the best IDS for drones with a heterogeneous embedded platform. This strategy can be decomposed into two phases: (1) an offline phase to embed a subset of IDS implementations that cover a maximum of options, and (2) an online phase to select the best IDS implementation depending on the mission conditions. The second contribution is an extension of IDS-DEEP to support homogeneous (multi-core) parallel platforms, by leveraging on several cores to decrease latency of IDS model execution. Both strategies have been extensively validated experimentally using characterization results from several heterogeneous platforms (CPU, GPU, and FPGA), as well as a homogeneous parallel platform based on PULP. These contributions pave the way for safer and more efficient deployment of drone swarms in complex environments.