Many correctional facilities suffer from the smuggling of cell phones and other wireless devices into prison walls. In order to locate these devices for confiscation, we must be able to map intercepted signals to indoor locations within a few meter radius. We chose to use cell phones of varying models and multiple low-cost software-defined radios for this task. The different types of cell phones provide us with a more robust dataset for location fingerprinting due to the different transmitter hardware in each. Furthermore, the SDRs allow us to easily receive the raw IQ data from WiFi signals while being more cost-efficient for smaller facilities. This raw data is collected from a harsh prison-like environment in a grid pattern and associated with the location they were captured. An advanced machine learning network uses the raw signals as input and locations as labels in order to map the signals to their respective locations. The accuracy of our system is then compared and discussed against prior works in this field. These studies often use values other than the raw IQ data such as channel state information and received signal strength indicator. Therefore, we augment our original input with each of these values and measure their effect on the system’s overall performance. The end result provides prisons with a tool capable of locating devices used in unauthorized zones for confiscation.
Wireless devices identify themselves using media access control (MAC) addresses which can be easily intercepted and mimicked by an adversary. Mobile devices also have a unique physical fingerprint represented by perturbations in the frequency of broadcasted signals caused by differences in the manufacturing process of their hardware components. This unique fingerprint is much more difficult to mimic. The short time Fourier transform (STFT) is used to analyze how the frequency content of a signal changes over time, and may provide a better representation of mobile signals in order to detect their unique fingerprint. In this paper, we have collected wireless signals using the 802.11 a/g protocol, showing the effect on classification performance of applying the STFT when varying the choice of window lengths, augmenting the data with complex Gaussian noise, and concatenating STFTs of different frequency resolutions, achieving state-of-the-art performance of 99.94% accuracy in the process.
We consider the problem of accurately detecting signals from contraband WiFi devices. Source locations may be selected in a worst-case fashion from within an indoor structure, such as a correctional facility. The structure layout is known, but inaccessible prior to deployment, and only a small number of detectors are available for sensing these signals. Our approach treats this setting as a covering problem, where the aim is to achieve a high probability of detection at each of the grid points of the terrain. Unlike prior approaches, we employ (1) a variant of the maximum coverage problem, which allows us to account for aggregate coverage by several detectors, and (2) a state-of-the-art commercial wireless simulator to provide SINR measurements that inform our problem instances. This approach is formulated as a mathematical program to which additional constraints are added to limit the number of detectors. Solving the program produces a placement of detectors whose performance is then evaluated for classifier accuracy. We present preliminary results, combining both simulation data and real-world data to evaluate the performance our approach against two competitors inspired by the literature.
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.
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.
KEYWORDS: Sensors, Ray tracing, Transceivers, Computer simulations, Detection and tracking algorithms, Signal detection, 3D modeling, Signal attenuation, Neural networks, Receivers
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.
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