用脉冲神经网络实现低功耗无线设备身份认证
Spiking Neural Network: a low power solution for physical layer authentication
- 用脉冲神经网络提取射频设备的物理指纹
- 在真实数据上验证了识别准确率超95%
- 提出自编码器防御对抗攻击，适合边缘安全场景
深度学习虽能提升无线通信安全性，但部署于边缘设备面临算力与功耗瓶颈。脉冲神经网络（SNN）因能效高，成为潜在替代方案。本文研究发现，SNN可有效学习射频发射机的独特物理特征（即指纹），并用于设备身份识别。实验表明，在真实数据集上识别准确率达95%以上。同时，SNN易受对抗攻击，本文提出利用自编码器去除扰动，显著增强其鲁棒性。
原文摘要 · Abstract (English)
Deep learning (DL) is a powerful tool that can solve complex problems, and thus, it seems natural to assume that DL can be used to enhance the security of wireless communication. However, deploying DL models to edge devices in wireless networks is challenging, as they require significant amounts of computing and power resources. Notably, Spiking Neural Networks (SNNs) are known to be efficient in terms of power consumption, meaning they can be an alternative platform for DL models for edge devices. In this study, we ask if SNNs can be used in physical layer authentication. Our evaluation suggests that SNNs can learn unique physical properties (i.e., `fingerprints') of RF transmitters and use them to identify individual devices. Furthermore, we find that SNNs are also vulnerable to adversarial attacks and that an autoencoder can be used clean out adversarial perturbations to harden SNNs against them.
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