Authentification par empreinte radio pour l’IoT

The Internet of Things (IoT) defines the objects connected to the Internet, which integrate sensors and/or actuators. Their proliferation considerably increases the risk of cyber threats. Therefore, it is necessary to propose countermeasures in order to secure their communications, especially at the physical layer. One of the possibilities consists in ensuring the authenticity of the messages transmitted by using Radio Frequency Fingerprinting (RFF). Particularly, this type of method exploits the imperfections of the components of a radio transmitter, or even those of the propagation channel, which are considered as unique. The approach defended in this manuscript consists in studying the properties necessary for the use of the RFF in an IoT context, but also the methods which result from it, as well as their implementations. First, three properties were introduced: adaptability, scalability and complexity. Then, two RFF methods taking into account these properties have been proposed: the siamese networks for RFF and the RF eigenfingerprints method. These present a compromise between scalability and complexity, allowing possibility to address different use cases. Finally, the implementation of these methods was considered, either within a network or on IoT device.L’internet des objets ou Internet of Things(IoT) désigne les objets reliés à internet, qui intègrent des capteurs et/ou des actionneurs. Leur prolifération augmente considérablement le risque de cybermenaces, il est donc nécessaire de proposer des contre-mesures afin de sécuriser leurs communications, notamment au niveau de la couche physique. Une des possibilités consiste à s’assurer de l’authenticité des messages transmis en utilisant de l’authentification par empreinte radio ou Radio Frequency Fingerprinting (RFF). Plus particulièrement, ce type de méthodes exploitent les imperfections des composants d’un émetteur radio, voire celles du canal de propagation, qui sont considérées comme uniques. L’approche défendue dans ce manuscrit consiste à étudier les propriétés nécessaires à l’utilisation du RFF dans un contexte IoT, mais aussi les méthodes qui en découlent, ainsi que leurs implémentations. Tout d’abord, trois propriétés ont été introduites : l’adaptabilité, la scalabilité et la complexité. Ensuite, deux méthodes de RFF prenant en compte ces propriétés ont été proposées : les réseaux siamois pour le RFF et les RF eigenfingerprints. Celles-ci présentent notamment un compromis entre scalabilité et complexité, permettant d’adresser différents cas applicatifs. Pour finir, l’implémentation de ces méthodes a été considérée, que ce soit au sein d’un réseau ou au niveau d’un appareil IoT

Authentification par empreinte radio pour l’IoT

The Internet of Things (IoT) defines the objects connected to the Internet, which integrate sensors and/or actuators. Their proliferation considerably increases the risk of cyber threats. Therefore, it is necessary to propose countermeasures in order to secure their communications, especially at the physical layer. One of the possibilities consists in ensuring the authenticity of the messages transmitted by using Radio Frequency Fingerprinting (RFF). Particularly, this type of method exploits the imperfections of the components of a radio transmitter, or even those of the propagation channel, which are considered as unique. The approach defended in this manuscript consists in studying the properties necessary for the use of the RFF in an IoT context, but also the methods which result from it, as well as their implementations. First, three properties were introduced: adaptability, scalability and complexity. Then, two RFF methods taking into account these properties have been proposed: the siamese networks for RFF and the RF eigenfingerprints method. These present a compromise between scalability and complexity, allowing possibility to address different use cases. Finally, the implementation of these methods was considered, either within a network or on IoT device.L’internet des objets ou Internet of Things (IoT) désigne les objets reliés à internet, qui intègrent des capteurs et/ou des actionneurs. Leur prolifération augmente considérablement le risque de cybermenaces, il est donc nécessaire de proposer des contre-mesures afin de sécuriser leurs communications, notamment au niveau de la couche physique. Une des possibilités consiste à s’assurer de l’authenticité des messages transmis en utilisant de l’authentification par empreinte radio ou Radio Frequency Fingerprinting (RFF). Plus particulièrement, ce type de méthodes exploitent les imperfections des composants d’un émetteur radio, voire celles du canal de propagation, qui sont considérées comme uniques. L’approche défendue dans ce manuscrit consiste à étudier les propriétés nécessaires à l’utilisation du RFF dans un contexte IoT, mais aussi les méthodes qui en découlent, ainsi que leurs implémentations. Tout d’abord, trois propriétés ont été introduites : l’adaptabilité, la scalabilité et la complexité. Ensuite, deux méthodes de RFF prenant en compte ces propriétés ont été proposées : les réseaux siamois pour le RFF et les RF eigenfingerprints. Celles-ci présentent notamment un compromis entre scalabilité et complexité, permettant d’adresser différents cas applicatifs. Pour finir, l’implémentation de ces méthodes a été considérée, que ce soit au sein d’un réseau ou au niveau d’un appareil IoT

Radio frequency fingerprinting in IoT context

L’internet des objets ou Internet of Things (IoT) désigne les objets reliés à internet, qui intègrent des capteurs et/ou des actionneurs. Leur prolifération augmente considérablement le risque de cybermenaces, il est donc nécessaire de proposer des contre-mesures afin de sécuriser leurs communications, notamment au niveau de la couche physique. Une des possibilités consiste à s’assurer de l’authenticité des messages transmis en utilisant de l’authentification par empreinte radio ou Radio Frequency Fingerprinting (RFF). Plus particulièrement, ce type de méthodes exploitent les imperfections des composants d’un émetteur radio, voire celles du canal de propagation, qui sont considérées comme uniques. L’approche défendue dans ce manuscrit consiste à étudier les propriétés nécessaires à l’utilisation du RFF dans un contexte IoT, mais aussi les méthodes qui en découlent, ainsi que leurs implémentations. Tout d’abord, trois propriétés ont été introduites : l’adaptabilité, la scalabilité et la complexité. Ensuite, deux méthodes de RFF prenant en compte ces propriétés ont été proposées : les réseaux siamois pour le RFF et les RF eigenfingerprints. Celles-ci présentent notamment un compromis entre scalabilité et complexité, permettant d’adresser différents cas applicatifs. Pour finir, l’implémentation de ces méthodes a été considérée, que ce soit au sein d’un réseau ou au niveau d’un appareil IoT.The Internet of Things (IoT) defines the objects connected to the Internet, which integrate sensors and/or actuators. Their proliferation considerably increases the risk of cyber threats. Therefore, it is necessary to propose countermeasures in order to secure their communications, especially at the physical layer. One of the possibilities consists in ensuring the authenticity of the messages transmitted by using Radio Frequency Fingerprinting (RFF). Particularly, this type of method exploits the imperfections of the components of a radio transmitter, or even those of the propagation channel, which are considered as unique. The approach defended in this manuscript consists in studying the properties necessary for the use of the RFF in an IoT context, but also the methods which result from it, as well as their implementations. First, three properties were introduced: adaptability, scalability and complexity. Then, two RFF methods taking into account these properties have been proposed: the siamese networks for RFF and the RF eigenfingerprints method. These present a compromise between scalability and complexity, allowing possibility to address different use cases. Finally, the implementation of these methods was considered, either within a network or on IoT device

Siamese Network on I/Q Signal for RF Fingerprinting

International audienceRF Fingerprinting techniques aim to authenticate a wireless emitter by the imperfections due to these components. It can be useful for authentication and network management for the future IoT networks. Various methods has been proposed using hand-crafted features and classic machine learning but nowadays many researchers try to apply deep learning architectures for RF Fingeprinting. Our contribution is based on Siamese Network, a deep learning architecture widely used by the face recognition community. We use the deep learning architectures proposed by the RF Fingeprinring community which processes the I/Q (In-phase and Quadrature) signal and the siamese network learning paradigms developed for the facial recognition to propose siamese architectures for RF Fingerprinting. One of the main advantage of the siamese network is the possibility to use one-shot learning and its ability to require a few data for the final implementation of the network. In this paper, we explain our implementation, our results and discuss about the potential benefits of our approach for final implementation in a wireless network

From Modeling to Sensing of Micro-Doppler in Radio Communications

The Doppler effect in radio systems has been widely explored by the radio communication community. However, these studies have been limited to simple motion such as linear translation. This paper presents a model for the Doppler modulation effect, i.e., the effect of complex movement on the received signal, using a geometrical approach. Particularly, we focused on studying micro-Doppler in radio communications produced by vibrations. Exploiting this phenomenon would allow the performance of passive micro-Doppler effect sensing based on communication. In this paper, we also propose signal processing techniques to detect the presence of the micro-Doppler effect and to estimate its parameters. Then, we present some experiments which highlight the micro-Doppler effect in a radio communication context. Finally, the end of the paper discusses some potential applications that exploit this phenomenon

From Modeling to Sensing of Micro-Doppler in Radio Communications

The Doppler effect in radio systems has been widely explored by the radio communication community. However, these studies have been limited to simple motion such as linear translation. This paper presents a model for the Doppler modulation effect, i.e., the effect of complex movement on the received signal, using a geometrical approach. Particularly, we focused on studying micro-Doppler in radio communications produced by vibrations. Exploiting this phenomenon would allow the performance of passive micro-Doppler effect sensing based on communication. In this paper, we also propose signal processing techniques to detect the presence of the micro-Doppler effect and to estimate its parameters. Then, we present some experiments which highlight the micro-Doppler effect in a radio communication context. Finally, the end of the paper discusses some potential applications that exploit this phenomenon

Drone Detection and Classification Using Physical-Layer Protocol Statistical Fingerprint

International audienceWe propose a novel approach for drone detection and classification based on RF communication link analysis. Our approach analyses large signal record including several packets and can be decomposed of two successive steps: signal detection and drone classification. On one hand, the signal detection step is based on Power Spectral Entropy (PSE), a measure of the energy distribution uniformity in the frequency domain. It consists of detecting a structured signal such as a communication signal with a lower PSE than a noise one. On the other hand, the classification step is based on a so-called physical-layer protocol statistical fingerprint (PLSPF). This method extracts the packets at the physical layer using hysteresis thresholding, then computes statistical features for classification based on extracted packets. It consists of performing traffic analysis of communication link between the drone and its controller. Conversely to classic drone traffic analysis working at data link layer (or at upper layers), it performs traffic analysis directly from the corresponding I/Q signal, i.e., at the physical layer. The approach shows interesting properties such as scale invariance, frequency invariance, and noise robustness. Furthermore, the classification method allows us to distinguish WiFi drones from other WiFi devices due to underlying requirement of drone communications such as good reactivity in control. Finally, we propose different experiments to highlight theses properties and performances. The physical-layer protocol statistical fingerprint exploiting communication specificities could also be used in addition of RF fingerprinting method to perform authentication of devices at the physical-layer

RF eigenfingerprints, an Efficient RF Fingerprinting Method in IoT Context

International audienceIn IoT networks, authentication of nodes is primordial and RF fingerprinting is one of the candidates as a non-cryptographic method. RF fingerprinting is a physical-layer security method consisting of authenticated wireless devices using their components’ impairments. In this paper, we propose the RF eigenfingerprints method, inspired by face recognition works called eigenfaces. Our method automatically learns important features using singular value decomposition (SVD), selects important ones using Ljung–Box test, and performs authentication based on a statistical model. We also propose simulation, real-world experiment, and FPGA implementation to highlight the performance of the method. Particularly, we propose a novel RF fingerprinting impairments model for simulation. The end of the paper is dedicated to a discussion about good properties of RF fingerprinting in IoT context, giving our method as an example. Indeed, RF eigenfingerprint has interesting properties such as good scalability, low complexity, and high explainability, making it a good candidate for implementation in IoT context

Characterizing Intrusion Detection Systems On Heterogeneous Embedded Platforms

International audienceSwarms of drones are more and more used for critical missions and need to be protected against malicious users. Intrusion Detection Systems (IDS) are used to analyze network traffic in order to detect possible threats. Modern IDSs rely on machine learning models for such a sake. Because of the absence of central management in swarms of drones, IDSs constitute a good second-line protective measure. Investigating the execution of IDS (resource-hungry) algorithms on drone (resource-constrained) devices is crucial when it comes to optimizing energy, response time, memory footprint and algorithm precision. In addition, embedded platforms used in drones often incorporate heterogeneous computing platforms on which IDSs could be executed.In this paper, we present a methodology and results about characterizing the execution of different IDS models on various platform (CPUs, GPUs). In effect, as swarm of drones operate in different mission contexts (e.g. criticity level) and states (e.g. energy budget, memory footprint), it is important to explore which IDS model to run on which platforms for a given mission in a given context. For this sake, we evaluated several metrics on different platforms: energy and resource consumption, accuracy for malicious traffic detection and response time. The models tested (RF, CNN, DNN) have shown different performance according to the measured metrics and the chosen platform and proved to be relevant in different mission states

New Methods for Fast Detection for Embedded Cognitive Radio

International audienceSpectrum Sensing is an important part of Cognitive Radio (CR) process. It can be used to determine if a Primary User (PU) (i.e. a licensed user) is emitting or not in the communication channel. This paper presents and compares three types of FFT-based detection algorithms for LTE-Advanced (LTE-A) cellular network at Orthogonal Frequency Division Multiple Access (OFDMA) level. These detectors sense the usage of the minimum time-frequency called Resource Block (RB). They are also low latency detectors and they only need one particular Orthogonal Frequency Division Multiplexing (OFDM) symbol to detect the usage of one RB. The three new detectors are based respectively on energy, correlation, and one what will be called eogration which combines energy and correlation. We analyze them with the Fisher's ratio and simulations of hypothesis test. The computing complexity of these detectors is also theoretically analyzed to provide guidance for future implementations