Robust Wireless Fingerprinting via Complex-Valued Neural Networks
Robust Wireless Fingerprinting via Complex-Valued Neural Networks
复制标题
通过复值神经网络实现稳健的无线指纹识别
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Upamanyu Madhow
中科院分区：
文献类型：
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作者：
S. Gopalakrishnan;Metehan Cekic;Upamanyu Madhow
利用每个设备独特的硬件缺陷的“无线指纹”是一种潜在的无线安全工具。这样的指纹应该能够区分发送相同消息的设备，并应与标准欺骗技术相当强大。由于无线信号中的信息存在于复杂的基带中，因此在本文中，我们探讨了使用具有复杂权重的神经网络使用监督学习来学习指纹。我们证明，尽管使用信号的各个部分具有潜在的好处，而不仅仅是序言学习指纹，但在可能的情况下，网络会作弊，但使用诸如发射机ID（可以轻松欺骗的信息）来人为地膨胀性能。我们还表明，通过插入额外的白色高斯噪声来增加噪音会导致显着的性能增长，这表明这种反直觉策略有助于学习更多强大的指纹。我们为两种不同的无线协议（WiFi和ADS-B）提供了结果，证明了该方法的有效性。
A "wireless fingerprint" which exploits hardware imperfections unique to each device is a potentially powerful tool for wireless security. Such a fingerprint should be able to distinguish between devices sending the same message, and should be robust against standard spoofing techniques. Since the information in wireless signals resides in complex baseband, in this paper, we explore the use of neural networks with complex- valued weights to learn fingerprints using supervised learning. We demonstrate that, while there are potential benefits to using sections of the signal beyond just the preamble to learn fingerprints, the network cheats when it can, using information such as transmitter ID (which can be easily spoofed) to artificially inflate performance. We also show that noise augmentation by inserting additional white Gaussian noise can lead to significant performance gains, which indicates that this counter-intuitive strategy helps in learning more robust fingerprints. We provide results for two different wireless protocols, WiFi and ADS-B, demonstrating the effectiveness of the proposed method.