Federated Intrusion Detection In NG-IoT Healthcare Systems: An Adversarial Approach
Ilias Siniosoglou, Panagiotis Sarigiannidis, Vasilis Argyriou, Θωμάς Λάγκας, Sotirios K. Goudos, Maria Poveda
Abstract
In recent years and with the advancement of IoT networks, malicious intrusions aiming at disrupting the services and getting access to confidential information in medical environments is ever progressing. To that end, this paper proposes a Federated Layered Architecture to be used in Medical Cyber-Physical Systems (MCPS) Networks that entails the creation of multiple aggregation layers to induce further security to the model training process. Moreover, two Deep Adversarial Neural Networks (GANs) are presented for use with data found in the MCPS environment. The evaluation of the presented work showed that the models trained in the Federated system have an increase in their ability to detect possible intrusions in the MCPS network than the commonly trained models.