Joint Radio Frequency Fingerprints Identification via Multi-antenna Receiver

In Internet of Things (IoT), radio frequency fingerprints (RFF) technology has been widely used for passive security authentication to identify the special emitter. However, few works took advantage of independent oscillator distortions at the receiver side, and no work has yet considered filtering receiver distortions. In this paper, we investigate the RFF identification (RFFI) involving unknown receiver distortions, where the phase noise caused by each antenna oscillator is independent. Three RFF schemes are proposed according to the number of receiving antennas. When the number is small, the Mutual Information Weighting Scheme (MIWS) is developed by calculating the weighted voting of RFFI result at each antenna; when the number is moderate, the Distortions Filtering Scheme (DFS) is developed by filtering out the channel noise and receiver distortions; when the number is large enough, the Group-Distortions Filtering and Weighting Scheme (GDFWS) is developed, which integrates the advantages of MIWS and DFS. Furthermore, the ability of DFS to filter out the channel noise and receiver distortions is theoretically analyzed at a specific confidence level. Experiments are provided when both channel noise and receiver distortions exist, which verify the effectiveness and robustness of the proposed schemes

EXTRINSIC CHANNEL-LIKE FINGERPRINT EMBEDDING FOR TRANSMITTER AUTHENTICATION IN WIRELESS SYSTEMS

We present a physical-layer fingerprint-embedding scheme for wireless signals, focusing on multiple input multiple output (MIMO) and orthogonal frequency division multiplexing (OFDM) transmissions, where the fingerprint signal conveys a low capacity communication suitable for authenticating the transmission and further facilitating secure communications. Our system strives to embed the fingerprint message into the noise subspace of the channel estimates obtained by the receiver, using a number of signal spreading techniques. When side information of channel state is known and leveraged by the transmitter, the performance of the fingerprint embedding can be improved. When channel state information is not known, blind spreading techniques are applied. The fingerprint message is only visible to aware receivers who explicitly preform detection of the signal, but is invisible to receivers employing typical channel equalization. A taxonomy of overlay designs is discussed and these designs are explored through experiment using time-varying channel-state information (CSI) recorded from IEEE802.16e Mobile WiMax base stations. The performance of the fingerprint signal as received by a WiMax subscriber is demonstrated using CSI measurements derived from the downlink signal. Detection performance for the digital fingerprint message in time-varying channel conditions is also presented via simulation

Cooperative Localization in Mines Using Fingerprinting and Neural Networks

This work is a special investigation in the localization of users in underground and confined areas such as gold mines. It sheds light on the basic approaches that are used nowadays to estimate the position and track users using wireless technology. Localization or Geo-location in confined and underground areas is one of the topics under research in mining labs and industries. The position of personnel and equipments in areas such as mines is of high importance because it improves industrial safety and security. Due to the special nature of underground environments, signals transmitted in a mine gallery suffer severe multipath effects caused by reflection, refraction, diffraction and collision with humid rough surfaces. In such cases and in cases where the signals are blocked due to the non-line of sight (NLOS) regions, traditional localization techniques based on the RSS, AOA and TOA/TDOA lead to high position estimation errors. One of the proposed solutions to such challenging situations is based on extracting the channel impulse response fingerprints with reference to one wireless receiver and using an artificial neural network as the matching algorithm. In this work we study this approach in a multiple access network where multiple access points are present. The diversity of the collected fingerprints allows us to create artificial neural networks that work separately or cooperatively using the same localization technique. In this approach, the received signals by the mobile at various distances are analysed and several components of each signal are extracted accordingly. The channel impulse response found at each position is unique to the position of the receiver. The parameters extracted from the CIR are the received signal strength, mean excess delay, root mean square, maximum excess delay, the number of multipath components, the total power of the received signal, the power of the first arrival and the delay of the first arrival. The use of multiple fingerprints from multiple references not only adds diversity to the set of inputs fed to the neural network but it also enhances the overall concept and makes it applicable in a multi-access environment. Localization is analyzed in the presence of two receivers using several position estimation procedures. The results showed that using two CIRs in a cooperative localization technique gives a position accuracy less than or equal to 1m for 90% of both trained and untrained neural networks. Another way of using cooperative intelligence is by using the time domain including tracking, probabilities and previous positions to the localization system. Estimating new positions based on previous positions recorded in history has a great improvement factor on the accuracy of the localization system where it showed an estimation error of less than 50cm for 90% of training data and 65cm for testing data. The details of those techniques and the estimation errors and graphs are fully presented and they show that using cooperative artificial intelligence in the presence of multiple signatures from different reference points as well as using tracking improves significantly the accuracy, precision, scalability and the overall performance of the localization system

Enhanced Indoor Localization System based on Inertial Navigation

An algorithm for indoor localization of pedestrians using an improved Inertial Navigation system is presented for smartphone based applications. When using standard inertial navigation algorithm, errors in sensors due to random noise and bias result in a large drift from the actual location with time. Novel corrections are introduced for the basic system to increase the accuracy by counteracting the accumulation of this drift error, which are applied using a Kalman filter framework. A generalized velocity model was applied to correct the walking velocity and the accuracy of the algorithm was investigated with three different velocity models which were derived from the actual velocity measured at the hip of walking person. Spatial constraints based on knowledge of indoor environment were applied to correct the walking direction. Analysis of absolute heading corrections from magnetic direction was performed . Results show that the proposed method with Gaussian velocity model achieves competitive accuracy with a 30\% less variance over Step and Heading approach proving the accuracy and robustness of proposed method. We also investigated the frequency of applying corrections and found that a 4\% corrections per step is required for improved accuracy. The proposed method is applicable in indoor localization and tracking applications based on smart phone where traditional approaches such as GNSS suffers from many issues

A robust, reliable and deployable framework for In-vehicle security

Cyber attacks on financial and government institutions, critical infrastructure, voting systems, businesses, modern vehicles, etc., are on the rise. Fully connected autonomous vehicles are more vulnerable than ever to hacking and data theft. This is due to the fact that the protocols used for in-vehicle communication i.e. controller area network (CAN), FlexRay, local interconnect network (LIN), etc., lack basic security features such as message authentication, which makes it vulnerable to a wide range of attacks including spoofing attacks. This research presents methods to protect the vehicle against spoofing attacks. The proposed methods exploit uniqueness in the electronic control unit electronic control unit (ECU) and the physical channel between transmitting and destination nodes for linking the received packet to the source. Impurities in the digital device, physical channel, imperfections in design, material, and length of the channel contribute to the uniqueness of artifacts. I propose novel techniques for electronic control unit (ECU) identification in this research to address security vulnerabilities of the in-vehicle communication. The reliable ECU identification has the potential to prevent spoofing attacks launched over the CAN due to the inconsideration of the message authentication. In this regard, my techniques models the ECU-specific random distortion caused by the imperfections in digital-to-analog converter digital to analog converter (DAC), and semiconductor impurities in the transmitting ECU for fingerprinting. I also model the channel-specific random distortion, impurities in the physical channel, imperfections in design, material, and length of the channel are contributing factors behind physically unclonable artifacts. The lumped element model is used to characterize channel-specific distortions. This research exploits the distortion of the device (ECU) and distortion due to the channel to identify the transmitter and hence authenticate the transmitter.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/154568/1/Azeem Hafeez Final Disseration.pdfDescription of Azeem Hafeez Final Disseration.pdf : Dissertatio

Advances in Computer Recognition, Image Processing and Communications, Selected Papers from CORES 2021 and IP&C 2021

As almost all human activities have been moved online due to the pandemic, novel robust and efficient approaches and further research have been in higher demand in the field of computer science and telecommunication. Therefore, this (reprint) book contains 13 high-quality papers presenting advancements in theoretical and practical aspects of computer recognition, pattern recognition, image processing and machine learning (shallow and deep), including, in particular, novel implementations of these techniques in the areas of modern telecommunications and cybersecurity

Authorized and rogue device discrimination using dimensionally reduced RF-DNA fingerprints for security purposes in wireless communication systems

La nature des réseaux de capteurs sans fil comme ZigBee, permettant la communication entre différents types de nœuds du réseau, les rend très vulnérables à divers types de menaces. Dans différentes applications des technologies sans fil modernes comme SmartHome, les informations privées et sensibles produites par le réseau peuvent être transmises au monde extérieur par des moyens filaires ou sans fil. Outre les avantages offerts, cette intégration augmentera certainement les exigences en matière de protection des communications. Les nœuds capteurs du réseau étant souvent placés à proximité d'autres appareils, le réseau peut être plus vulnérable aux attaques potentielles. Cette recherche de doctorat a pour but d'utiliser les attributs natifs distincts de radiofréquence RF-DNA sécurisés produits par le processus d'empreinte numérique dans le but de fournir un support de communication sans fil sécurisé pour les communications de réseau ZigBee. Ici, nous visons à permettre une discrimination d'appareil en utilisant des préambules physiques (PHY) extraits des signaux émis pas de différents appareils. Grâce à cette procédure, nous pouvons établir une distinction entre différents appareils produits par différents fabricants ou par le même fabricant. Dans un tel cas, nous serons en mesure de fournir aux appareils des identifications physiques de niveau binaire non clonables qui empêchent l'accès non autorisé des appareils non autorisés au réseau par la falsification des identifications autorisées.The nature of wireless networks like ZigBee sensors, being able to provide communication between different types of nodes in the network makes them very vulnerable to various types of threats. In different applications of modern wireless technologies like Smart Home, private and sensitive information produced by the network can be conveyed to the outside world through wired or wireless means. Besides the advantages, this integration will definitely increase the requirements in the security of communications. The sensor nodes of the network are often located in the accessible range of other devices, and in such cases, a network may face more vulnerability to potential attacks. This Ph.D. research aims to use the secure Radio Frequency Distinct Native Attributes (RF-DNA) produced by the fingerprinting process to provide a secure wireless communication media for ZigBee network device communications. Here, we aim to provide device discrimination using Physical (PHY) preambles extracted from the signal transmitted by different devices. Through this procedure, we are able to distinguish between different devices produced by different manufacturers, or by the same one. In such cases, we will be able to provide devices with unclonable physical bit-level identifications that prevent the unauthorized access of rogue devices to the network through the forgery of authorized devices' identifications

Wireless device identification from a phase noise prospective

As wireless devices become increasingly pervasive and essential, they are becoming both a target for attacks and the very weapon with which such an attack can be carried out. Wireless networks have to face new kinds of intrusion that had not been considered previously because they are linked to the open nature of wireless networks. In particular, device identity management and intrusion detection are two of the most significant challenges in any network security solution but they are paramount for any wireless local area networks (WLANs) because of the inherent non-exclusivity of the transmission medium. The physical layer of 802.11-based wireless communication does not offer security guarantee because any electromagnetic signal transmitted can be monitored, captured, and analyzed by any sufficiently motivated and equipped adversary within the 802.11 device's transmission range. What is required is a form of identification that is nonmalleable (cannot be spoofed easily). For this reason we have decided to focus on physical characteristics of the network interface card (NIC) to distinguish between different wireless users because it can provide an additional layer of security. The unique properties of the wireless medium are extremely useful to get an additional set of information that can be used to extend and enhance traditional security mechanisms. This approach is commonly referred to as radio frequency fingerprinting (RFF), i.e., determining specific characteristics (fingerprint) of a network device component. More precisely, our main goal is to prove the feasibility of exploiting phase noise in oscillators for fingerprinting design and overcome existing limitations of conventional approaches. The intuition behind our design is that the autonomous nature of oscillators among noisy physical systems makes them unique in their response to perturbations and none of the previous work has ever tried to take advantage of thi

Super Resolution Algorithms for Indoor Positioning Systems

Ph.DDOCTOR OF PHILOSOPH