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.

中文翻译：

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SLIP：基于自监督学习的模型反转和基于中毒​​检测的车载网络零信任系统

通信网络的进步及其与机器学习技术的集成为消费者提供无处不在的基于预测的服务铺平了道路。然而，这些服务会考虑敏感和私有数据来训练机器学习模型。随着模型反转和投毒攻击的出现，敏感和私密数据可能被泄露，这成为通信网络大规模自动化服务实现的障碍。零信任技术允许网络对参与服务提供任务的数据进行评级，但现有的工作没有考虑零信任服务的模型隐私。本文提出了一种基于自监督学习的模型反转和中毒（SLIP）检测框架，该框架可以对模型进行评级，以便网络可以决定模型是否适合提供服务或已被破坏。该框架利用多种生成式人工智能技术，例如生成对抗网络（GAN）和扩散模型，以实现其在联邦学习环境中的实施。实验结果表明，SLIP框架有助于将车辆网络的模型反转和中毒攻击分别减少16.4%和13.2%。