Wireless communication is susceptible to security breaches by adversarial actors mimicking Media Access Controller (MAC) addresses of currently-connected devices. Classifying devices by their “physical fingerprint” can help to prevent this problem since the fingerprint is unique for each device and independent of the MAC address. Previous techniques have mapped the WiFi signal to real values and used classification methods that support solely real-valued inputs. In this paper, we put forth four new deep neural networks (NNs) for classifying WiFi physical fingerprints: a real-valued deep NN, a corresponding complex-valued deep NN, a real-valued deep CNN, and the corresponding complex-valued deep convolutional NN (CNN). Results show state-of-the-art performance against a dataset of nine WiFi devices.
In wireless networks, MAC-address spoofing is a common attack that allows an adversary to gain access to the system. To circumvent this threat, previous work has focused on classifying wireless signals using a “physical fingerprint”, i.e., changes to the signal caused by physical differences in the individual wireless chips. Instead of relying on MAC addresses for admission control, fingerprinting allows devices to be classified and then granted access. In many network settings, the activity of legitimate devices—those devices that should be granted access— may be dynamic over time. Consequently, when faced with a device that comes online, a robust fingerprinting scheme must quickly identify the device as legitimate using the pre-existing classification, and meanwhile identify and group those unauthorized devices based on their signals. This paper presents a two-stage Zero-Shot Learning (ZSL) approach to classify a received signal originating from either a legitimate or unauthorized device. In particular, during the training stage, a classifier is trained for classifying legitimate devices. The classifier learns discriminative features and the outlier detector uses these features to classify whether a new signature is an outlier. Then, during the testing stage, an online clustering method is applied for grouping those identified unauthorized devices. Our approach allows 42% of unauthorized devices to be identified as unauthorized and correctly clustered.
Signal attributes such as angle of arrival (AoA), time of arrival (ToA), signal amplitude, and phase can be used by a set of receivers (detectors) to perform location fingerprinting (LF), whereby the location of a wireless source is determined. In validating new approaches for location fingerprinting, it is useful to simulate these attributes for the subset of signals that intersect detectors. However, given indoor settings with a complex architecture, it is computationally expensive to simulate multipath propagation while preserving detailed signal information. Moreover, this cost can be unnecessary since determining whether an LF approach is promising may not require tracing all rays that impact the detector. Here, we report on our preliminary efforts to design and test a MATLAB-based simulation tool for wireless propagation that addresses this issue. Our approach builds upon well-known ray-tracing techniques, but innovates via an algorithm designed to obtain a sizable subset of rays that intersect a detector, along with the AoA, ToA, signal amplitude, and phase for each such ray. Finally, we employ our tool in conjunction with a neural network-based method for location fingerprinting, demonstrating the intended use case for our simulation tool.