Over-the-Air Distributed Neural Network in Internet of Things with Threat Modeling for Replay Attacks

Large scale distributed neural networks have demonstrated promise for various inference tasks in Internet of Things (IoT) devices, including intelligent security monitoring and defense against network threats. However, the massive amounts of data generated by IoT applications and limited computational capabilities present significant challenges in implementing typical applications, such as secure protocols for data confidentiality. Over-The-Air (OTA) computation, a recently proposed physical layer computing architecture, has great potential to address these issues. In this paper, we propose an OTA distributed neural network with the mutual benefit of joint computing and communication. However, the open channel environment in which the network's forward computation is implemented renders OTA-based joint computing and communication methods vulnerable to replay attacks, thereby compromising the accuracy of the network performance and wasting valuable bandwidth resources due to backpropagation of contaminated information during OTA computing. A threat model of network is established to investigate the impact of replay attacks during the iterative process. Our analysis and numerical results demonstrate that the replay attacks have a significantly impact on the network. Specifically, the test accuracy rate decreases from 85 to 35%, and the convergence rate decreases by an average of $$40\%$$ . When the number of iterations is set to 500, the success probability of replay attacks is 0.378.