DeepRadioID：基于深度学习的无线电指纹识别算法的实时的容错优化
DeepRadioID: Real-Time Channel-Resilient Optimization of Deep Learning-based Radio Fingerprinting Algorithms
摘要：
无线电指纹识别提供了一种可靠且节能的物联网身份验证策略。通过将输入映射到一个非常大的特征空间，深度学习算法可以被训练成对在任何无线标准下运行的大量设备进行指纹识别。无线电指纹识别中最关键的挑战之一是抵消无线信道的作用，无线信道的作用通过破坏硬件损伤而显著降低指纹识别的准确性。另一方面，由于其庞大的规模，深度学习算法很难在实时的【形】重新训练。另一个有待研究的方面是，攻击者是否可以成功地模仿另一个设备指纹。为了解决这些关键问题，本文提出了DeepRadioID，这是一种优化基于深度学习的无线电指纹识别算法准确性的系统，而无需重新训练底层深度学习模型。我们在由20个名义上相同的软件定义无线电组成的实验测试平台上，以及由DARPA RFMLS计划提供的500个ADS-B设备和500个WiFi设备组成的两个数据集上广泛评估了DeepRadioID。实验结果表明，DeepRadioID（i）在考虑的三种情况下将指纹识别准确率提高了约35%，50%和58%;（ii）当试图通过使用过滤器模仿其他设备指纹时，将对手的准确率降低了约54%;（iii）在100台设备数据集上实现了27%的改进。
英文摘要：
Radio fingerprinting provides a reliable and energy-efficient IoT authentication strategy. By mapping inputs onto a very large feature space, deep learning algorithms can be trained to fingerprint large populations of devices operating under any wireless standard. One of the most crucial challenges in radio fingerprinting is to counteract the action of the wireless channel, which decreases fingerprinting accuracy significantly by disrupting hardware impairments. On the other hand, due to their sheer size, deep learning algorithms are hardly re-trainable in real-time. Another aspect that is yet to be investigated is whether an adversary can successfully impersonate another device fingerprint. To address these key issues, this paper proposes DeepRadioID, a system to optimize the accuracy of deep-learning-based radio fingerprinting algorithms without retraining the underlying deep learning model. We extensively evaluate DeepRadioID on a experimental testbed of 20 nominally-identical software-defined radios, as well as on two datasets made up by 500 ADS-B devices and by 500 WiFi devices provided by the DARPA RFMLS program. Experimental results show that DeepRadioID (i) increases fingerprinting accuracy by about 35%, 50% and 58% on the three scenarios considered; (ii) decreases an adversary's accuracy by about 54% when trying to imitate other device fingerprints by using their filters; (iii) achieves 27% improvement over the state of the art on a 100-device dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
