论文标题
通过深层对抗性神经网络学习有安全的调制
Learning Secured Modulation With Deep Adversarial Neural Networks
论文作者
论文摘要
对利用异质设备使用无线频谱的兴趣越来越迫使我们重新考虑物理层安全性，以保护传输的波形免受窃听器的影响。我们提出了一种使用调制技术的端到端对称密钥神经加密和解密算法，该技术仍然由窃听器未能确定，配备了相同的神经网络，并与预期用户相同的数据集进行了培训。我们将加密和调制求解为关节问题，将其映射到复杂的模拟信号，而无需粘附任何特定的加密算法或调制技术。我们训练我们在信任的神经网络之间合作学习加密和解密算法，而Eavesdropper的模型则在对手方面受到相同数据的对手训练，以最大程度地减少错误。我们引入了一个具有定义梯度的离散激活层，以在有损通道中打击噪声。我们的结果表明，一对受信任的用户可以在干净和嘈杂的频道中交换数据位，在那里训练有素的对手无法破译数据。
Growing interest in utilizing the wireless spectrum by heterogeneous devices compels us to rethink the physical layer security to protect the transmitted waveform from an eavesdropper. We propose an end-to-end symmetric key neural encryption and decryption algorithm with a modulation technique, which remains undeciphered by an eavesdropper, equipped with the same neural network and trained on the same dataset as the intended users. We solve encryption and modulation as a joint problem for which we map the bits to complex analog signals, without adhering to any particular encryption algorithm or modulation technique. We train to cooperatively learn encryption and decryption algorithms between our trusted pair of neural networks, while eavesdropper's model is trained adversarially on the same data to minimize the error. We introduce a discrete activation layer with a defined gradient to combat noise in a lossy channel. Our results show that a trusted pair of users can exchange data bits in both clean and noisy channels, where a trained adversary cannot decipher the data.
