SpaceProtoNet : Revealing Unknown Protocols' Origin in Space Communications

ABSTRACT

Unknown custom protocols in space communication often complicate security analysis. A key challenge in protocol reverse engineering (PRE) is identifying the original protocol that a new variant is based on—a critical step that is often manual and time‐consuming. This paper presents

, a novel framework that employs a convolutional neural network (CNN) to classify protocol types from image representations of raw packet data. Experimental results demonstrate that effectively generalises from known protocols to classify unseen variants into their correct base families, showing an F1‐score of 0.96 even when only four packets are available. The framework also maintains robustness in adverse conditions, sustaining an F1‐score of 0.91 even with a 10% bit error rate (BER). By automating this crucial identification step, provides a systematic foundation for PRE, where the predicted base protocol family can help narrow the analysis scope and reduce the complexity of security analysis for space communication systems.