OATGA: Optimizing Adversarial Training via Genetic Algorithm for Automatic Modulation Classification

Recently, with the explosive growth of the mobile devices, spectrum sensing for wireless devices has become an attractive research. Automatic modulation classification (AMC) is an important task in spectrum sensing and plays an important role in blind signal recognition, and deep learning has been shown to greatly improve the performance of AMC networks. However, deep learning models for AMC are considered vulnerable against adversarial attacks, resulting in unreliable sensor systems. In this paper, we study how to deal with the threats of adversarial attacks by optimizing neural networks. We propose an adversarial defense method based on genetic algorithm (GA) to optimize adversarial training. The optimizations are performed between the layers of the neural networks to obtain the weights with maximum fitness, to improve the adversarial robustness of the models. In addition, an indicator to quantitatively evaluate the adversarial robustness of the models is also proposed. We conduct experiments in the different perturbation-to-noise ratios (PNRs) to verify the effectiveness of the defensive models. The results show that the GA-optimized approach can greatly improve the classification accuracy of the models to adversarial examples, and provides a better fitting ability than the mainstream adversarial training methods.