HyperAdv: Dynamic Defense Against Adversarial Radio Frequency Machine Learning Systems

Radio Frequency Machine Learning Systems (RFMLS) have attracted increasing interest over the past few years. However, it has been demonstrated that RFMLS are vulnerable to Adversarial Machine Learning (AML). While AML has been extensively investigated in traditional domains, current state of the art often compromises the performance on benign data or introduces excessive computational overhead. As such, it cannot meet the strict requirements of tactical RFMLS. In this paper, we propose a novel defense approach based on dynamic adaptation of Deep Neural Network (DNN). Specifically, we leverage a hypernetwork to dynamically generate diverse parameters for a target DNN during inference. In addition, an ensemble learning and multi-stage training framework is proposed to train such a hypernetwork. Experimental results show that the proposed defense can increase the accuracy on adversarial examples by 48% and 16% in comparison to naturally trained DNN and defensive training strategies, respectively.