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Radio frequency fingerprint identification (RFFI) is a promising device authentication technique based on transmitter hardware impairments. The device-specific hardware features can be extracted at the receiver by analyzing the received signal and used for authentication. In this paper, we propose a scalable and channel-robust RFFI framework achieved by deep learning powered radio frequency fingerprint (RFF) extractor and channel independent features. Specifically, we leverage deep metric learning to train an RFF extractor, which has excellent generalization ability and can extract RFFs from previously unseen devices. Any devices can be enrolled via the pre-trained RFF extractor and the RFF database can be maintained efficiently for allowing devices to join and leave. Wireless channel impacts the RFF extraction and is tackled by exploiting channel independent features and data augmentation. We carried out extensive experimental evaluation involving 60 commercial off-the-shelf LoRa devices and a USRP N210 software defined radio platform. The results have successfully demonstrated that our framework can achieve excellent generalization abilities for rogue device detection and device classification as well as effective channel mitigation. [ABSTRACT FROM AUTHOR]

We propose a key generation protocol with the aid of a reconfigurable intelligent surface (RIS) to boost secret key rate (SKR) in quasi-static environments. Considering a passive eavesdropper, we derive the closed-form expression of the lower and upper bounds of the SKR. Our findings indicate the SKR is determined by the number of RIS elements, the correlation coefficient, the pilot length and the quality of the reflecting channel. Our protocol fully exploits the randomness from the direct channel and the reflecting channel. Monte Carlo simulations validate the analytical expression of the SKR and demonstrate our protocol outperforms existing work. [ABSTRACT FROM AUTHOR]

Radio frequency fingerprint identification (RFFI) is an emerging device authentication technique that relies on the intrinsic hardware characteristics of wireless devices. This paper designs a deep learning-based RFFI scheme for Long Range (LoRa) systems. Firstly, the instantaneous carrier frequency offset (CFO) is found to drift, which could result in misclassification and significantly compromise the stability of the deep learning-based RFFI system. CFO compensation is demonstrated to be effective mitigation. Secondly, three signal representations for deep learning-based RFFI are investigated in time, frequency, and time-frequency domains, namely in-phase and quadrature (IQ) samples, fast Fourier transform (FFT) results and spectrograms, respectively. For these signal representations, three deep learning models are implemented, i.e., multilayer perceptron (MLP), long short-term memory (LSTM) network and convolutional neural network (CNN), in order to explore an optimal framework. Finally, a hybrid classifier that can adjust the prediction of deep learning models with the estimated CFO is designed to further increase the classification accuracy. The CFO will not change dramatically over several continuous days, hence it can be used to correct predictions when the estimated CFO is much different from the reference one. Experimental evaluation is performed in real wireless environments involving 25 LoRa devices and a Universal Software Radio Peripheral (USRP) N210 platform. The spectrogram-CNN model is found to be optimal for classifying LoRa devices which can reach an accuracy of 96.40% with the least complexity and training time. [ABSTRACT FROM AUTHOR]

Node identification based on unique hardware features like clock skews has been considered an efficient technique in wireless sensor networks (WSNs). Spoofing attacks imitating unique hardware features, however, could significantly impair or break down conventional clock-skew-based node identification due to exposed clock information through broadcasting. To defend against Spoofing attacks, we propose a new node identification scheme called node identification against Spoofing attack (NISA). It utilizes the reverse time synchronization framework, where sensor nodes’ clock skews are estimated at the head of a WSN, and the spatially-correlated radio link information to achieve simultaneous node identification and attack detection. We further provide centralized and distributed NISA for covering both single-hop and multi-hop scenarios, the former of which employs a single-input and multiple-output convolutional neural network. With a real WSN testbed consisting of TelosB sensor nodes running TinyOS, we investigate the identifiability of clock skews under temperature and voltage variations and evaluate the performance of both centralized and distributed NISA. Experimental results demonstrate that both centralized and distributed NISA could provide accurate node identification and Spoofing attack detection. [ABSTRACT FROM AUTHOR]

Device authentication is essential for securing Internet of things. Radio frequency fingerprint identification (RFFI) is an emerging technique that exploits intrinsic and unique hardware impairments as the device identifier. The existing RFFI literature focuses on experimental exploration but comprehensive modelling is missing. This paper systematically models impairments of transmitter and receiver in narrowband systems and carries out extensive experiments and simulations to evaluate their effects on RFFI. The modelled impairments include oscillator imperfections, imbalance of inphase (I) and quadrature (Q) branches of mixers and power amplifier (PA) nonlinearity. We then propose a convolutional neural network-based RFFI protocol. We carry out experimental measurements over three months and demonstrate that oscillator imperfections are not suitable for RFFI due to their unpredictable time variation caused by temperature change. Our simulation results show that our protocol can classify 50 and 200 devices with uniformly and randomly distributed IQ imbalances and PA nonlinearities with high accuracy, namely 99% and 89%, respectively. We also show that the RFFI has some tolerance on different receiver imbalances during training and classification. Specifically, the accuracy is shown to degrade less than 20% when the residual receiver’s gain and phase imbalances are small. Based on the experimental and simulation results, we made recommendations for designing a robust RFFI protocol, namely compensate carrier frequency offset and calibrate IQ imbalances of receivers. [ABSTRACT FROM AUTHOR]

Physical-layer key generation (PKG) based on channel reciprocity has recently emerged as a new technique to establish secret keys between devices. Most works focus on pairwise communication scenarios with single or small-scale antennas. However, the fifth generation (5G) wireless communications employ massive multiple-input multiple-output (MIMO) to support multiple users simultaneously, bringing serious overhead of reciprocal channel acquisition. This paper presents a multi-user secret key generation in massive MIMO wireless networks. We provide a beam domain channel model, in which different elements represent the channel gains from different transmit directions to different receive directions. Based on this channel model, we analyze the secret key rate and derive a closed-form expression under independent channel conditions. To maximize the sum secret key rate, we provide the optimal conditions for the Kronecker product of the precoding and receiving matrices and propose an algorithm to generate these matrices with pilot reuse. The proposed optimization design can significantly reduce the pilot overhead of the reciprocal channel state information acquisition. Furthermore, we analyze the security under the channel correlation between user terminals (UTs), and propose a low overhead multi-user secret key generation with non-overlapping beams between UTs. Simulation results demonstrate the near-optimal performance of the proposed precoding and receiving matrices design and the advantages of the non-overlapping beam allocation. [ABSTRACT FROM AUTHOR]

This paper proposes a novel deep learning-based radio frequency fingerprint (RFF) identification method for internet of things (IoT) terminal authentications. Differential constellation trace figure (DCTF), a two-dimensional (2D) representation of differential relationship of signal time series, is utilized to extract RFF features without requiring any synchronization. A convolutional neural network (CNN) is then designed to identify different devices using DCTF features. Compared to the existing CNN-based RFF identification methods, the proposed DCTF-CNN possesses the merits of high identification accuracy, zero prior information and low complexity. Experimental results have demonstrated that the proposed DCTF-CNN can achieve an identification accuracy as high as 99.1% and 93.8% under SNR levels of 30 dB and 15 dB, respectively, when classifying 54 target ZigBee devices, which significantly outperforms the existing RFF identification methods. [ABSTRACT FROM AUTHOR]

A low-complexity, yet secure framework is proposed for protecting the IoT and for achieving both authentication and secure communication. In particular, the slight random difference among transceivers is extracted for creating a unique radio frequency fingerprint and for ascertaining the unique user identity. The wireless channel between any two users is a perfect source of randomness and can be exploited as cryptographic keys. This can be applied to the physical layer of the communications protocol stack. This article reviews these protocols and shows how they can be integrated to provide a complete IoT security framework. We conclude by outlining the future challenges in applying these compelling physical layer security techniques to the IoT. [ABSTRACT FROM AUTHOR]

Secret key generation exploits the unique random features of wireless channels, hence, it is eminently suitable for the resource constrained Internet of Things applications. However, it has only been involved for single links between a pair of users, whilst there is a paucity of the literature on group and multi-user key generation. This paper proposes an orthogonal frequency-division multiple access (OFDMA) based multi-user key generation protocol to efficiently establish keys in a star topology. The uplink and downlink multi-user access facilitated by OFDMA allows the central node to simultaneously communicate with multiple users, which can significantly reduce the channel probing overhead. In particular, we provide a compelling case study of multi-user secret key generation by designing a prototype based on IEEE 802.11ax, a new Wi-Fi standard to be released. Our simulation results have demonstrated that the OFDMA-based multi-user key generation protocol incurs low interference amongst the users, whilst benefiting from channel reciprocity and generating unique random keys. [ABSTRACT FROM AUTHOR]

This correspondence paper investigates key generation performance in environments with large scale fading including path loss and shadow fading. The path loss is found to be affected only by the distance between users and not secure for key generation. The shadow fading effect is caused by large obstacles such as buildings. The correlation relationships of the shadow fading are modelled and demonstrated to meet the requirements of key generation principles. Monte Carlo Simulations have been carried out and validated that shadow fading-based key generation is feasible and secure. [ABSTRACT FROM AUTHOR]

In this paper, directional modulation (DM) transmitters, designed for orthogonal frequency-division multiplexing (OFDM) wireless data transfer are proposed using time-modulated arrays. It is shown that by properly designing the switch controlling time sequences the proposed time-modulated OFDM DM systems exhibit promising features, such as 1) requires only one radio frequency chain, 2) the conventional efficient OFDM signal construction approach, i.e., by using inverse fast Fourier transform modules, can be adopted, 3) it is “DM synthesis-free,” meaning that there is no need to resynthesize the array excitation vectors for different secure communication directions, different modulation schemes, and different DM power efficiencies, and 4) the tradeoff between secrecy performance and power efficiency can be flexibly adjusted. [ABSTRACT FROM AUTHOR]

Physical layer security key generation exploits unpredictable features from wireless channels to achieve high security, which requires high reciprocity in order to set up symmetric keys between two users. This paper investigates enhancing the channel reciprocity using a loop-back scheme with multiple frequency bands in time-division duplex (TDD) communication systems, in order to mitigate the effect of hardware fingerprint interference and synchronization offset. The scheme is evaluated to be robust to passive eavesdropping and active Man-in-the-Middle attack through both theoretical analyses and practical measurements. A secret key generation protocol is subsequently designed. The performance of the proposed secret key generation method is then evaluated through both numerical simulation and experiments. Results demonstrate that the proposed scheme can effectively mitigate non-reciprocity and outperforms the classical TDD scheme in both key disagreement rate and key generation rate. [ABSTRACT FROM AUTHOR]

This correspondence paper presents automatic key generation for long-range wireless communication in low power wide area networks (LPWANs), employing LoRa as a case study. Differential quantization is adopted to extract a high level of randomness. Experiments conducted both in an outdoor urban environment and in an indoor environment demonstrate that this key generation technique is applicable for LPWANs and shows that it is able to reliably generate secure keys. [ABSTRACT FROM AUTHOR]

Physical layer-based key generation encounters the reciprocity bottleneck when applied in frequency-division duplexing (FDD) systems. This letter constructs the reciprocal channel characteristics of the uplink and downlink transmissions for key generation in FDD systems. The frequency dependence of propagation characteristics in both large-scale fading and small-scale fading is investigated. A general channel model is then established by considering frequency impact. A reciprocal channel construction framework is finally created. Numerical results demonstrate that our algorithm can construct bidirectional channel coefficients with high correlation and thus enable key generation technology to be used in FDD systems. [ABSTRACT FROM AUTHOR]

Radio frequency fingerprint (RFF) is an intrinsic hardware characteristic and has been employed for device identification. Its application in low signal-to-noise-ratio (SNR) has never been explored because its identification performance is greatly affected by the received signal quality. This letter proposes a novel RFF identification scheme for spread spectrum systems in low SNR scenarios. In the scheme, a signal preprocessing method, information data estimation-based stacking algorithm, is proposed, which leverages the repeated spreading sequences and stacks them together to eliminate the noise and interference effect. Simulation results demonstrate that the proposed scheme can achieve 98% identification rate when the received signal SNR is −15 dB and the length of spreading sequence is 1023. [ABSTRACT FROM AUTHOR]