To date, the Medical Internet of Things (MIoT) technology has been recognized and widely applied due to its convenience and practicality. The MIoT enables the application of machine learning to predict diseases of various kinds automatically and accurately, assisting and facilitating effective and efficient medical treatment. However, the MIoT are vulnerable to cyberattacks which have been constantly advancing. In this paper, we establish a MIoT platform and demonstrate a scenario where a trained Convolutional Neural Network (CNN) model for predicting lung cancer complicated with pulmonary embolism can be attacked. First, we use CNN to build a model to predict lung cancer complicated with pulmonary embolism and obtain high detection accuracy. Then, we build a copycat model using only a small amount of data labeled by the target network, aiming to steal the established prediction model. Experimental results prove that the stolen model can also achieve a relatively high prediction outcome, revealing that the copycat network could successfully copy the prediction performance from the target network to a large extent. This also shows that such a prediction model deployed on MIoT devices can be stolen by attackers, and effective prevention strategies are open questions for researchers.

中文翻译：

---

智能医疗平台上对智能移动设备的神经网络模型偷窃攻击

迄今为止，由于其便利性和实用性，医疗物联网（MIoT）技术已得到认可并得到广泛应用。MIoT使机器学习的应用能够自动，准确地预测各种疾病，从而帮助并促进有效而高效的医疗。但是，MIoT易受不断发展的网络攻击的影响。在本文中，我们建立了一个MIoT平台，并演示了可以攻击训练有素的卷积神经网络（CNN）模型预测肺癌并发肺栓塞的场景。首先，我们使用CNN建立预测肺癌并发肺栓塞的模型，并获得较高的检测准确性。然后，我们仅使用目标网络标记的少量数据来构建模仿模型，旨在窃取已建立的预测模型。实验结果证明，被盗模型还可以实现较高的预测结果，这表明模仿网络可以在很大程度上复制目标网络的预测性能。这也表明，部署在MIoT设备上的这种预测模型可能会被攻击者窃取，有效的预防策略是研究人员的未解决问题。