Distributed intrusion detection system for CubeSats, based on deep learning packets classification model

Abstract

As part of the significant evolution that the space industry is experiencing, a fast increase in the number of CubeSats projects for scientific, commercial and military purposes has been noted in recent years. This acceleration, coupled with the widespread use of Commercial Off-The-Shelf (COTS) components, raises questions about the ability of these systems to withstand potential cyberattacks, which are becoming more prevalent. Thus, the cyber resilience of a CubeSat depends on its ability to effectively detect attacks despite the constraints of autonomy and the limitation of resources that characterize the space missions. To address this need, our paper proposes an Intrusion Detection System (IDS) for CubeSat systems. This distributed solution uses an Artificial Neural Network (ANN) module for classifying CSP packets over CAN on board the space segment based respectively on timestamp and Data field, while the classifier training processes are executed at the ground segment level. The results obtained following the experimentation of this IDS against three types of common attacks are very encouraging thanks to detection rates obtained between 87.66% and 99.59% (F1-score).