273 research outputs found
Passive Integrated Sensing and Communication Scheme based on RF Fingerprint Information Extraction for Cell-Free RAN
This paper investigates how to achieve integrated sensing and communication
(ISAC) based on a cell-free radio access network (CF-RAN) architecture with a
minimum footprint of communication resources. We propose a new passive sensing
scheme. The scheme is based on the radio frequency (RF) fingerprint learning of
the RF radio unit (RRU) to build an RF fingerprint library of RRUs. The source
RRU is identified by comparing the RF fingerprints carried by the signal at the
receiver side. The receiver extracts the channel parameters from the signal and
estimates the channel environment, thus locating the reflectors in the
environment. The proposed scheme can effectively solve the problem of
interference between signals in the same time-frequency domain but in different
spatial domains when multiple RRUs jointly serve users in CF-RAN architecture.
Simulation results show that the proposed passive ISAC scheme can effectively
detect reflector location information in the environment without degrading the
communication performance.Comment: 11 pages, 6 figures, submitted on 28-Feb-2023, China Communication,
Accepted on 14-Sep-202
Towards Adaptive RF Fingerprint-based Authentication of IIoT devices
As IoT technologies mature, they are increasingly finding their way into more
sensitive domains, such as Medical and Industrial IoT, in which safety and
cyber-security are of great importance. While the number of deployed IoT
devices continues to increase exponentially, they still present severe
cyber-security vulnerabilities. Effective authentication is paramount to
support trustworthy IIoT communications, however, current solutions focus on
upper-layer identity verification or key-based cryptography which are often
inadequate to the heterogeneous IIoT environment. In this work, we present a
first step towards achieving powerful and flexible IIoT device authentication,
by leveraging AI adaptive Radio Frequency Fingerprinting technique selection
and tuning, at the PHY layer for highly accurate device authentication over
challenging RF environments
DSLN: Securing Internet of Things Through RF Fingerprint Recognition in Low-SNR Settings
The explosive growth of Internet of things (IoT) has mandated the security of data access. Although authentication methods can enhance network security, their vulnerability to malicious attacks may be a barrier for the wide deployments in IoT scenarios. To address the security issue, we advocate the use of physical layer security through radio-frequency (RF) fingerprint recognition. Observing that most RF fingerprint recognition methods show a degradation of performance under low signal-to-noise ratio (SNR) environments, we present a dynamic shrinkage learning network (DSLN) to enhance security for IoT applications, particularly in the setting of low SNR. We design a novel dynamic shrinkage threshold for improving the accuracy of recognition under low-SNR environments. Additionally, we design an identity shortcut for reducing the running time of RF fingerprint recognition. In comparison with convolutional neural network (CNN), recurrent neural network (RNN) and a hybrid CNN+RNN network (CRNN), our proposed DSLN yields accuracy improvements of up to 20%. Moreover, DSLN can reduce running time by up to 60%, indicating its great potential to a real-time IoT system, e.g., an intelligent automotive system
A Robust Zero-Calibration RF-based Localization System for Realistic Environments
Due to the noisy indoor radio propagation channel, Radio Frequency (RF)-based
location determination systems usually require a tedious calibration phase to
construct an RF fingerprint of the area of interest. This fingerprint varies
with the used mobile device, changes of the transmit power of smart access
points (APs), and dynamic changes in the environment; requiring re-calibration
of the area of interest; which reduces the technology ease of use. In this
paper, we present IncVoronoi: a novel system that can provide zero-calibration
accurate RF-based indoor localization that works in realistic environments. The
basic idea is that the relative relation between the received signal strength
from two APs at a certain location reflects the relative distance from this
location to the respective APs. Building on this, IncVoronoi incrementally
reduces the user ambiguity region based on refining the Voronoi tessellation of
the area of interest. IncVoronoi also includes a number of modules to
efficiently run in realtime as well as to handle practical deployment issues
including the noisy wireless environment, obstacles in the environment,
heterogeneous devices hardware, and smart APs. We have deployed IncVoronoi on
different Android phones using the iBeacons technology in a university campus.
Evaluation of IncVoronoi with a side-by-side comparison with traditional
fingerprinting techniques shows that it can achieve a consistent median
accuracy of 2.8m under different scenarios with a low beacon density of one
beacon every 44m2. Compared to fingerprinting techniques, whose accuracy
degrades by at least 156%, this accuracy comes with no training overhead and is
robust to the different user devices, different transmit powers, and over
temporal changes in the environment. This highlights the promise of IncVoronoi
as a next generation indoor localization system.Comment: 9 pages, 13 figures, published in SECON 201
Multi-Channel Attentive Feature Fusion for Radio Frequency Fingerprinting
Radio frequency fingerprinting (RFF) is a promising device authentication
technique for securing the Internet of things. It exploits the intrinsic and
unique hardware impairments of the transmitters for RF device identification.
In real-world communication systems, hardware impairments across transmitters
are subtle, which are difficult to model explicitly. Recently, due to the
superior performance of deep learning (DL)-based classification models on
real-world datasets, DL networks have been explored for RFF. Most existing
DL-based RFF models use a single representation of radio signals as the input.
Multi-channel input model can leverage information from different
representations of radio signals and improve the identification accuracy of the
RF fingerprint. In this work, we propose a novel multi-channel attentive
feature fusion (McAFF) method for RFF. It utilizes multi-channel neural
features extracted from multiple representations of radio signals, including IQ
samples, carrier frequency offset, fast Fourier transform coefficients and
short-time Fourier transform coefficients, for better RF fingerprint
identification. The features extracted from different channels are fused
adaptively using a shared attention module, where the weights of neural
features from multiple channels are learned during training the McAFF model. In
addition, we design a signal identification module using a convolution-based
ResNeXt block to map the fused features to device identities. To evaluate the
identification performance of the proposed method, we construct a WiFi dataset,
named WFDI, using commercial WiFi end-devices as the transmitters and a
Universal Software Radio Peripheral (USRP) as the receiver. ..
Improved Wireless Security through Physical Layer Protocol Manipulation and Radio Frequency Fingerprinting
Wireless networks are particularly vulnerable to spoofing and route poisoning attacks due to the contested transmission medium. Traditional bit-layer defenses including encryption keys and MAC address control lists are vulnerable to extraction and identity spoofing, respectively. This dissertation explores three novel strategies to leverage the wireless physical layer to improve security in low-rate wireless personal area networks. The first, physical layer protocol manipulation, identifies true transceiver design within remote devices through analysis of replies in response to packets transmitted with modified physical layer headers. Results herein demonstrate a methodology that correctly differentiates among six IEEE 802.15.4 transceiver classes with greater than 99% accuracy, regardless of claimed bit-layer identity. The second strategy, radio frequency fingerprinting, accurately identifies the true source of every wireless transmission in a network, even among devices of the same design and manufacturer. Results suggest that even low-cost signal collection receivers can achieve greater than 90% authentication accuracy within a defense system based on radio frequency fingerprinting. The third strategy, based on received signal strength quantification, can be leveraged to rapidly locate suspicious transmission sources and to perform physical security audits of critical networks. Results herein reduce mean absolute percentage error of a widely-utilized distance estimation model 20% by examining signal strength measurements from real-world networks in a military hospital and a civilian hospital
Challenges of Radio Frequency Fingerprinting: From Data Collection to Deployment
Radio Frequency Fingerprinting (RFF) techniques promise to authenticate
wireless devices at the physical layer based on inherent hardware imperfections
introduced during manufacturing. Such RF transmitter imperfections are
reflected into over-the-air signals, allowing receivers to accurately identify
the RF transmitting source. Recent advances in Machine Learning, particularly
in Deep Learning (DL), have improved the ability of RFF systems to extract and
learn complex features that make up the device-specific fingerprint. However,
integrating DL techniques with RFF and operating the system in real-world
scenarios presents numerous challenges. This article identifies and analyzes
these challenges while considering the three reference phases of any DL-based
RFF system: (i) data collection and preprocessing, (ii) training, and finally,
(iii) deployment. Our investigation points out the current open problems that
prevent real deployment of RFF while discussing promising future directions,
thus paving the way for further research in the area.Comment: 7 pages, 1 table, and 4 figure
Using RF-DNA Fingerprints to Discriminate ZigBee Devices in an Operational Environment
This research was performed to expand AFIT\u27s Radio Frequency Distinct Native Attribute (RF-DNA) fingerprinting process to support IEEE 802.15.4 ZigBee communication network applications. Current ZigBee bit-level security measures include use of network keys and MAC lists which can be subverted through interception and spoofing using open-source hacking tools. This work addresses device discrimination using Physical (PHY) waveform alternatives to augment existing bit-level security mechanisms. ZigBee network vulnerability to outsider threats was assessed using Receiver Operating Characteristic (ROC) curves to characterize both Authorized Device ID Verification performance (granting network access to authorized users presenting true bit-level credentials) and Rogue Device Rejection performance (denying network access to unauthorized rogue devices presenting false bit-level credentials). Radio Frequency Distinct Native Attribute (RF-DNA) features are extracted from time-domain waveform responses of 2.4 GHz CC2420 ZigBee transceivers to enable humanlike device discrimination. The fingerprints were constructed using a hybrid pool of emissions collected under a range of conditions, including anechoic chamber and an indoor office environment where dynamic multi-path and signal degradation factors were present. The RF-DNA fingerprints were input to a Multiple Discriminant Analysis, Maximum Likelihood (MDA/ML) discrimination process and a 1 vs. many Looks most like? classification assessment made. The hybrid MDA model was also used for 1 vs. 1 Looks how much like? verification assessment. ZigBee Device Classification performance was assessed using both full and reduced dimensional fingerprint sets. Reduced dimensional subsets were selected using Dimensional Reduction Analysis (DRA) by rank ordering 1) pre-classification KS-Test p-values and 2) post-classification GRLVQI feature relevance values. Assessment of Zigbee device ID verification capability
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