SLIP: Self-Supervised Learning Based Model Inversion and Poisoning Detection-Based Zero-Trust Systems for Vehicular Networks

The advances in communication networks and their integration with machine learning technology have paved the way for ubiquitous and prediction-based services for consumers. However, these services consider sensitive and private data for training a machine learning model. With the emergence of model inversion and poisoning attacks, sensitive and private data can be leaked, which is a hindrance for the realization of largescale automation services concerning communication networks. Zero-trust techniques allow the networks to rate the data for their participation in service provisioning tasks, but existing works do not consider model privacy for the zero-trust services. This article proposes a Self-supervised Learning based model Inversion and Poisoning (SLIP) detection framework that enables the rating of model so that network could decide whether the model is suitable for service provisioning or has been compromised. The framework leverages several Generative AI technologies such as generative adversarial networks (GANs) and diffusion models, to realize its implementation in federated learning setting. Experimental results show that the SLIP framework helps in reducing model inversion and poisoning attacks by 16.4% and 13.2% for vehicular networks, respectively.