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. ..

Detecting ADS-B Spoofing Attacks using Deep Neural Networks

The Automatic Dependent Surveillance-Broadcast (ADS-B) system is a key component of the Next Generation Air Transportation System (NextGen) that manages the increasingly congested airspace. It provides accurate aircraft localization and efficient air traffic management and also improves the safety of billions of current and future passengers. While the benefits of ADS-B are well known, the lack of basic security measures like encryption and authentication introduces various exploitable security vulnerabilities. One practical threat is the ADS-B spoofing attack that targets the ADS-B ground station, in which the ground-based or aircraft-based attacker manipulates the International Civil Aviation Organization (ICAO) address (a unique identifier for each aircraft) in the ADS-B messages to fake the appearance of non-existent aircraft or masquerade as a trusted aircraft. As a result, this attack can confuse the pilots or the air traffic control personnel and cause dangerous maneuvers. In this paper, we introduce SODA - a two-stage Deep Neural Network (DNN)-based spoofing detector for ADS-B that consists of a message classifier and an aircraft classifier. It allows a ground station to examine each incoming message based on the PHY-layer features (e.g., IQ samples and phases) and flag suspicious messages. Our experimental results show that SODA detects ground-based spoofing attacks with a probability of 99.34%, while having a very small false alarm rate (i.e., 0.43%). It outperforms other machine learning techniques such as XGBoost, Logistic Regression, and Support Vector Machine. It further identifies individual aircraft with an average F-score of 96.68% and an accuracy of 96.66%, with a significant improvement over the state-of-the-art detector.Comment: Accepted to IEEE CNS 201

Learning Robust Radio Frequency Fingerprints Using Deep Convolutional Neural Networks

Radio Frequency Fingerprinting (RFF) techniques, which attribute uniquely identifiable signal distortions to emitters via Machine Learning (ML) classifiers, are limited by fingerprint variability under different operational conditions. First, this work studied the effect of frequency channel for typical RFF techniques. Performance characterization using the multi-class Matthews Correlation Coefficient (MCC) revealed that using frequency channels other than those used to train the models leads to deterioration in MCC to under 0.05 (random guess), indicating that single-channel models are inadequate for realistic operation. Second, this work presented a novel way of studying fingerprint variability through Fingerprint Extraction through Distortion Reconstruction (FEDR), a neural network-based approach for quantifying signal distortions in a relative distortion latent space. Coupled with a Dense network, FEDR fingerprints were evaluated against common RFF techniques for up to 100 unseen classes, where FEDR achieved best performance with MCC ranging from 0.945 (5 classes) to 0.746 (100 classes), using 73% fewer training parameters than the next-best technique

An Analysis of RF Transfer Learning Behavior Using Synthetic Data

Transfer learning (TL) techniques, which leverage prior knowledge gained from data with different distributions to achieve higher performance and reduced training time, are often used in computer vision (CV) and natural language processing (NLP), but have yet to be fully utilized in the field of radio frequency machine learning (RFML). This work systematically evaluates how radio frequency (RF) TL behavior by examining how the training domain and task, characterized by the transmitter/receiver hardware and channel environment, impact RF TL performance for an example automatic modulation classification (AMC) use-case. Through exhaustive experimentation using carefully curated synthetic datasets with varying signal types, signal-to-noise ratios (SNRs), and frequency offsets (FOs), generalized conclusions are drawn regarding how best to use RF TL techniques for domain adaptation and sequential learning. Consistent with trends identified in other modalities, results show that RF TL performance is highly dependent on the similarity between the source and target domains/tasks. Results also discuss the impacts of channel environment, hardware variations, and domain/task difficulty on RF TL performance, and compare RF TL performance using head re-training and model fine-tuning methods.Comment: arXiv admin note: substantial text overlap with arXiv:2206.0832

Automatic Estimation of Modulation Transfer Functions

The modulation transfer function (MTF) is widely used to characterise the performance of optical systems. Measuring it is costly and it is thus rarely available for a given lens specimen. Instead, MTFs based on simulations or, at best, MTFs measured on other specimens of the same lens are used. Fortunately, images recorded through an optical system contain ample information about its MTF, only that it is confounded with the statistics of the images. This work presents a method to estimate the MTF of camera lens systems directly from photographs, without the need for expensive equipment. We use a custom grid display to accurately measure the point response of lenses to acquire ground truth training data. We then use the same lenses to record natural images and employ a data-driven supervised learning approach using a convolutional neural network to estimate the MTF on small image patches, aggregating the information into MTF charts over the entire field of view. It generalises to unseen lenses and can be applied for single photographs, with the performance improving if multiple photographs are available

Advancements in Wi-Fi-Based Passenger Counting and Crowd Monitoring: Techniques and Applications

The widespread use of personal mobile devices, including tablets and smartphones, created new opportunities for collecting comprehensive data on individual movements within cities while preserving their anonymity. Extensive research focused on turning personal mobile devices into tools for measuring human presence. To protect privacy, the data collected must be anonymous or pseudo-anonymous, leading to the preference for management data. A common approach involves analysing probe requests, which are Wi-Fi protocol messages transmitted by mobile devices while searching for access points. These messages contain media access control (MAC) addresses, which used to be unique identifiers. To safeguard the privacy of smartphone users, the major manufacturers (Google, Apple, and Microsoft) have implemented algorithms that generate random MAC addresses, which change often and unpredictably. This thesis focuses on the problem of fingerprinting Wi-Fi devices based on analysing management messages to overcome previous methods that relied on the MAC address and became obsolete. Detecting messages from the same source allows counting the devices in an area, calculating their permanence, and approximating these metrics with the ones of the humans carrying them. An open dataset of probe requests with labelled data has been designed, built, and used to validate the experiments. The dataset is also provided with guidelines for collecting new data and extending it. Since the dataset contains records of individual devices, the first step of this study was simulating the presence of multiple devices by aggregating multiple records in sets. Many experiments have been conducted to enhance the accuracy of the clustering. The proposed techniques exploit features extracted from individual management messages and from groups of messages called bursts. Moreover, other experiments show what happens when one or more features are split into their components or when the logarithm of their value is used. Before running the algorithm, a feature selection was performed and exploited to improve the accuracy. The clustering methods considered are DBSCAN and OPTICS

Machine Learning based RF Transmitter Characterization in the Presence of Adversaries

The advances in wireless technologies have led to autonomous deployments of various wireless networks. As these networks must co-exist, it is important that all transmitters and receivers are aware of their radio frequency (RF) surroundings so that they can learn and adapt their transmission and reception parameters to best suit their needs. To this end, machine learning techniques have become popular as they can learn, analyze and even predict the RF signals and associated parameters that characterize the RF environment. In this dissertation, we address some of the fundamental challenges on how to effectively apply different learning techniques in the RF domain. In the presence of adversaries, malicious activities such as jamming, and spoofing are inevitable which render most machine learning techniques ineffective. To facilitate learning in such settings, we propose an adversarial learning-based approach to detect unauthorized exploitation of RF spectrum. First, we show the applicability of existing machine learning algorithms in the RF domain. We design and implement three recurrent neural networks using different types of cell models for fingerprinting RF transmitters. Next, we focus on securing transmissions on dynamic spectrum access network where primary user emulation (PUE) attacks can pose a significant threat. We present a generative adversarial net (GAN) based solution to counter such PUE attacks. Ultimately, we propose recurrent neural network models which are able to accurately predict the primary users\u27 activities in DSA networks so that the secondary users can opportunistically access the shared spectrum. We implement the proposed learning models on testbeds consisting of Universal Software Radio Peripherals (USRPs) working as Software Defined Radios (SDRs). Results reveal significant accuracy gains in accurately characterizing RF transmitters- thereby demonstrating the potential of our models for real world deployments

Doppler Radar Techniques for Distinct Respiratory Pattern Recognition and Subject Identification.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017