PP-GBD: Privacy-Preserving Graph Neural Botnet Detection in Encrypted Traffic

This work proposes PP-GBD, a unified privacy-preserving framework for botnet detection in encrypted traffic using graph neural networks and payload-independent metadata. Its key contribution is a formally calibrated fixed-slot graph-level differential privacy mechanism with explicit add/remove adjacency and sensitivity, integrated with federated learning and an HE-oriented polynomial GCN design. The study also provides a reproducible controlled benchmark and quantifies the privacy–utility trade-offs across non-private, differentially private, polynomial, and federated models.