论文标题
调整：朝向域 - 不稳定洛拉设备身份验证的便携式深度学习模型
Tweak: Towards Portable Deep Learning Models for Domain-Agnostic LoRa Device Authentication
论文作者
论文摘要
基于深度学习的设备指纹已成为仅通过捕获的RF传输来识别和身份验证设备的关键方法。常规方法无法移植到不同域，因为如果一个模型是对来自一个域的数据训练的，则它在来自不同但相关域的数据上不能很好地执行。此类域的示例包括用于收集数据的接收器硬件，捕获数据的日/时间以及设备的协议配置。这项工作提出了调整，这是一种使用公制学习和校准过程的技术，它使一个模型可以训练来自一个域的数据，可以很好地在另一个域的数据上表现出色。该过程仅通过来自目标域的少量培训数据而不改变模型的权重来完成，这使得技术在计算上轻巧，因此适用于资源有限的IoT网络。这项工作评估了调整的有效性相对于其在各种情况下使用真正的洛拉设备的测试台识别物联网设备的能力。该评估的结果表明，调整是可行的，对于具有有限的计算资源和应用程序具有时间敏感任务的网络尤其有用。
Deep learning based device fingerprinting has emerged as a key method of identifying and authenticating devices solely via their captured RF transmissions. Conventional approaches are not portable to different domains in that if a model is trained on data from one domain, it will not perform well on data from a different but related domain. Examples of such domains include the receiver hardware used for collecting the data, the day/time on which data was captured, and the protocol configuration of devices. This work proposes Tweak, a technique that, using metric learning and a calibration process, enables a model trained with data from one domain to perform well on data from another domain. This process is accomplished with only a small amount of training data from the target domain and without changing the weights of the model, which makes the technique computationally lightweight and thus suitable for resource-limited IoT networks. This work evaluates the effectiveness of Tweak vis-a-vis its ability to identify IoT devices using a testbed of real LoRa-enabled devices under various scenarios. The results of this evaluation show that Tweak is viable and especially useful for networks with limited computational resources and applications with time-sensitive missions.
