Spread spectrum communication plays a vital role in safeguarding the Internet of Things systems, due to its inherently low probability of interception and anti-jamming capability. However, conventional spread spectrum systems based on pseudorandom sequences are disadvantageous in limited sequence length and deterministic periodicity, making them vulnerable to brute-force attacks. To address these limitations and enhance the randomness of the spread spectrum sequences, a novel wireless channel randomness integrated spread spectrum sequence generation method is proposed in this work. Taking advantages of the intrinsic randomness, temporal variations, and unpredictability of wireless channel fadings, the proposed approach converts the extracted channel features into ordered sequences, which are then used to control the selection of irreducible generating polynomials for spread spectrum sequence generation. The proposed method improves the randomness and secrecy of the integrated spread spectrum sequence. Theoretical analysis and simulation results demonstrate that the proposed sequences not only achieve higher randomness entropy compared to the traditionalm-sequence, but also pass the National Institute of Standards and Technology randomness tests. Furthermore, performance evaluations under various signal-to-interference ratio conditions show improved autocorrelation properties and largely lower bit error rates, validating the effectiveness of the proposed method in improving the anti-jamming capability.

Camera-based visible light positioning (VLP) is a promising technique for accurate and low-cost indoor camera pose estimation (CPE). To reduce the number of required light-emitting diodes (LEDs), advanced methods commonly exploit LED shape features for positioning. Although interesting, they are typically restricted to a single LED geometry, leading to failure in heterogeneous LED-shape scenarios. To address this challenge, this paper investigates Lamé curves as a unified representation of common LED shapes and proposes a generic VLP algorithm using Lamé curve-shaped LEDs, termed LC-VLP. In the considered system, multiple ceiling-mounted Lamé curve-shaped LEDs periodically broadcast their curve parameters via visible light communication, which are captured by a camera-equipped receiver. Based on the received LED images and curve parameters, the receiver can estimate the camera pose using LC-VLP. Specifically, an LED database is constructed offline to store the curve parameters, while online positioning is formulated as a nonlinear least-squares problem and solved iteratively. To provide a reliable initialization, a correspondence-free perspective-n-points (FreePnP) algorithm is further developed, enabling approximate CPE without any pre-calibrated reference points. The performance of LC-VLP is verified by both simulations and experiments. Simulations show that LC-VLP outperforms state-of-the-art methods in both circular- and rectangular-LED scenarios. Compared to a perspective arcs algorithm, LC-VLP can achieve reductions of both over 30% in average position and rotation errors. Experiments further show that LC-VLP can achieve an average position accuracy of less than 4 cm.

The Border Gateway Protocol (BGP) is the core routing protocol for inter-domain routing, providing substantial flexibility but lacking convergence guarantees, which can lead to network oscillations and instability. Despite prior research proposing various methods to address these issues, existing algorithms still fall short in terms of accuracy and efficiency.In this paper, we introduce a novel and efficient algorithm that statically analyzes BGP configurations to accurately determine routing convergence. Our algorithm achieves several major advancements:•Superior Accuracy: It outperforms the state-of-the-art algorithm by correctly identifying a broader range of configurations.•Scalability: It is highly efficient, capable of analyzing Internet-scale configurations within polynomial time complexity.•Reliability: It eliminates false positives, ensuring that potentially oscillating configurations are never incorrectly reported as convergence.Our experimental results demonstrate that the proposed algorithm significantly enhances accuracy and efficiency compared to existing methods, making it highly suitable for large-scale Internet applications. This work represents a substantial step forward in ensuring the stability and reliability of BGP routing, addressing critical challenges in modern Internet infrastructure.

Polarization fingerprint (PF) is a promising technique for low-cost Internet of Things (IoT) device identification, which has better performance compared to radio frequency fingerprint (RFF). However, due to the manufacturing technology improvement, PF and RFF suffer from critical drawbacks, especially the reduction in fingerprint differences among devices. To solve this problem, we propose an artificial polarization fingerprint injection (APFI) scheme based on convenient antenna surface slotting. First, we find slotting can produce frequencydependent variation in polarization, which increases the fingerprint difference. Subsequently, we proposed the polarization frequency gradient matrix as a classification feature, based on the characteristics of the injected fingerprints, which eliminates the spatial instability of the original PFs. Extensive simulations and experiments demonstrate that APFI improves the device capability more than 7 times compared with the original PFs under 99.5% identification accuracy.

Information leakage through wireless channels poses a significant security concern within contemporary cellular networks, such as 5G new radio (NR). Among the myriad of potential attack vectors, passive traffic analysis (PTA) stands out as a pervasive and surreptitious threat, which allows attackers to discern the specific services utilized by unsuspecting victims without their noticing. In this work, we present a pioneering approach to achieve fine-grained service identification by adopting an unexplored perspective: mapping traffic transmission patterns to physical layer time-frequency occupancy patterns, which we refer to as Passive Time-Frequency Traffic (PTTF). Additionally, it selects the uplink control channel that carries the acknowledgment/negative acknowledgment (ACK/NACK) feedback within the Hybrid Automatic Repeat reQuest (HARQ) process as the data source. Statistical features of ACK/NACK time-frequency resources are extracted for traffic classification, and activities are recognized from a three-tier classification algorithm. For validation, we conduct field experiments targeting commercialized smartphones within the real-world operator’s network. This setup effectively mirrors practical scenarios, as the resources within the target frequency band can also be allocated to other equipment in the operator’s network. Furthermore, cross-validation experiments involving different smartphone brands and various network formats are conducted in order to ascertain the generalizability of the proposed PTTF.

This study presents a novel approach for physical layer authentication based on channel polarization response (CPR). CPR is sensitive to variation in the physical properties of scatterers, and the CPR difference between various channels is higher than the channel frequency response (CFR) under rich scattering scenarios. Additionally, the estimation of CPR is continuous, the authentication interval can be adjusted according to the channel coherence time, then the proposed scheme can be applied to any rich scattering scenarios, including highly dynamic scenarios. Since the received polarization state is fixed during the channel coherence time, we can coherently stack the received polarization state to improve the signal to noise ratio (SNR) and the estimation accuracy of CPR, thereby achieving high authentication accuracy under ultra-low SNR. Moreover, since the transmitted polarization state of various transmitters is different, because of their unique hardware deficiencies, and since the CPR is dependent on the transmitted polarization state, the CPR of other transmitters is different, allowing the resolution of co-located attacks. We theoretically drive the false alarm probability, detection probability, optimal discriminant threshold, computational complexity, optimal stacking numbers, and optimal CPR points for authentication. Furthermore, extensive simulations and experiments are performed to verify the validity and effectiveness of the proposed scheme.

Currently, radio frequency fingerprint (RFF), which describes the physical layer features of wireless signals in time-frequency domain, has been intensively studied. However, the research of polarization fingerprint (PF), which describes the physical layer features of wireless signals in the polarization domain, has just started. In this paper, PF is deeply studied, and a PF-based LoRaWAN physical layer authentication solution is designed under the actual application scenario. Firstly, the physical layer features in polarization are analyzed from the full path of polarization formation, propagation and reception, and the mathematical model of PF is constructed. There exist two main properties in PF: frequency characteristic and spatial characteristic. The spatial characteristic is the unique property of PF compared with RFF, which significantly improves the fingerprint discrimination of the same type of device. Subsequently, the solution has two authentication mechanisms according to different deployment strategies of LoRa devices: polarization fingerprint identification (PFI) based authentication and PF tracking-based authentication. PFI is implemented based on ensemble convolutional neural network (CNN), which assigns different weights to parts of PF with different discrimination to improve identification accuracy. PF tracking-based authentication solves the impact of LoRa device movement on authentication. Finally, the authentication performance and robustness of the solution are evaluated by experiments, and the ability of the solution to deal with typical attacks is analyzed. In summary, the PF-based LoRaWAN physical layer authentication solution provides considerable application potential.

In this paper, we propose a physical authentication scheme based on the full channel information (FCI) in dual-polarized antenna communication systems. Different from channel impulse response (CIR) and channel frequency response (CFR), which only characterize the spatial fading of the channel, FCI reflects the spatial fading and polarization fading of channel, and completely characterizes the channel. Specifically, we use the least square (LS) to estimate channel spatial fading and polarization fading separately. Then based on statistical signal processing and binary hypothesis model, we establish an authen-tication scheme based on FCI and theoretically deduce the false alarm probability, detection probability, and optimal threshold of the proposed scheme. Besides, we conduct numerous simulation analysis on the proposed scheme. The simulation results show that the proposed scheme has higher authentication accuracy than that of CFR based schemes, especially in the case of low SNR.

Radio frequency fingerprint (RFF) is a mature physical-layer identification technique for specific emitter identifi-cation (SEI). However, RFF faces the problem of low discrepancy. Therefore we propose polarization fingerprint (PF). PF has frequency characteristics derived from antenna hardware im-perfections and spatial characteristics derived from the vectorial properties of polarization. Based on these two characteristics, the mathematical model of PF is constructed. Because the spatial characteristics are not limited by the improvement of hardware manufacturing process, it still has a significant discrepancy. We also propose a spatial polarization compensation (SPC) for PF changes due to emitter movement. With spatial characteristics and SPC, PF can be effectively applied to SEI. However, the spatial characteristics also bring a trade-off between the identi-fication accuracy and the maximum emitter capacity. Finally, these two characteristics of PF and the proposed SPC are experimentally verified, then the identification accuracy of the proposed method is measured in real scenarios.

In this paper, we propose a physical layer authentication scheme based on channel polarization response (CPR) in low SNR scenes, such as IoT, 5G, and 6G communication systems. We use the transmitting polarization state as the “pilot” to estimate CPR. Then, using the property that the polarization state of the signal is independent of the transmission content, the coherent superposition is used to stack the signal piecewise to improve the SNR. We derive the false alarm probability, detection probability, optimal threshold, and computational complexity of the proposed scheme, and give the solution method of the optimal stacking numbers under specific SNR and authentication performance. Besides, we analyze the effectiveness of the proposed scheme through numerical simulation. Simulation results show that the proposed scheme can still achieve good authentication performance under ultra low SNR, such as when SNR = -20dB, the detection probability is 99.6%, and the false alarm probability is 2%.

Radio frequency (RF) fingerprinting is a low-cost, high-efficiency, and high-security authentication technique for wireless IoT devices with limited resources, but RF fingerprinting faces problems such as small fingerprint differences, low finger-print stability, and high implementation difficulty. In order to solve these problems, we propose a novel concept of polarization fingerprinting. Polarization fingerprint (PF) is manifested as the correlation between polarization state and frequency. The properties of PF include group feature, individual feature and directionality. Group feature characterizes the antenna structure, and individual feature characterizes the antenna hardware imperfections. The directionality comes from the vector property of polarization and contains the relative position information of the communicating devices. The directionality solves the problem of similar fingerprints that may occur when the number of devices increases, which cannot be solved by RF fingerprinting. Compared to RF fingerprint, PF can exist stably and continuously, which not only solves the problem of low fingerprint stability, but also makes polarization fingerprinting based authentication easier to be implemented. The stability and continuity of PF allow more samples to be obtained during authentication. We also proved that increasing the sample amount can reduce the false alarm rate of authentication. Finally, we conducted experiments based on wireless IoT devices. Experiment results show that polarization fingerprinting based authentication has better performance than RF fingerprinting based under the same conditions.

Apps installed on smartphones may reveal users' privacy, which is often under malicious attacks. Most privacy attacks are based on network layer traffic. However, the encryption used in the cellular network makes it difficult for a passive adversary to obtain the traffic. In this paper, we leverage the data link layer metadata, such as PDCP packet size, distribution, and interarrival time, to create Apps fingerprints and then conduct a non-intrusive smartphone Apps privacy attack. We test three different smartphones on a 4G LTE laboratory network. On twenty popular Apps selected from the AppStore and Huawei AppGallary, we achieve an Fl-score ranging from 91.32% to 99.49%. Also, we investigate the effect of classification algorithms, time windows, monitoring duration and smartphone brands on Apps fingerprinting attack. Furthermore, we evaluate the performance of the attack using only downlink traffic, which is consistent with the actual attack scenario. Because the data link layer specifications of 4G LTE and 5G are similar, the method of Apps fingerprinting attack can be extended to the latest 5G networks.

Computer displays emit electromagnetic waves, which compromise the information displayed by the computer. This can be a potential information security threat as the sensitive information can be stolen from a distance without leaving any trace. The video leakage signals contain the information of the image displayed in the computer, so the video leakage signals can be seen as special image signals. However, different from the normal image signal, the signal to noise ratio (SNR) of video leakage signal is low due to the environmental noise and many other man-made noises. In this paper, a novel wavelet based independent component analysis (ICA) method is proposed for improving SNR of computer video leakage signals. By using this method, we can improve the performance of pre-processing of computer video leaking signals. We solve the problem of using Fast ICA in processing video leakage signal by using a wavelet filter. The performance of Fast ICA is improved by working in wavelet domain because of advantages like ease of implementation and less computation time when compared to time domain. We pre-process one-dimensional received signal without reconstructing the image since it is inefficient to reconstruct signal before processing. A direct SNR can't be defined. Therefore, another metric called quasi signal to noise ratio (QSNR) is defined to estimate signal to noise ratio of video leakage signals. The processed results of the actual experimental data show that the proposed wavelet based ICA algorithm has a better performance than the Fast ICA algorithm and the Wavelet denoising algorithm.

Modulation recognition has been an essential part in non-cooperative communications. Without knowing any prior knowledge of the transmitted data, the received signals may be affected by intersymbol interference (ISI), carrier frequency offset, phase offset and timing delay. ISI and these unknown parameters would degrade the performance of many recognition methods. There have been many robust estimators of carrier frequency offset, phase offset and timing delay. However, the robust recognition methods combating ISI have not been well designed. Most blind channel equalization methods, which are designed to overcome the effects of ISI, have poor robustness to noise and carrier frequency offset. In this paper, a robust recognition scheme is proposed to recognize phase-shift keying (PSK) and quadrature amplitude modulation (QAM) signals in non-cooperative communication with ISI environment. The envelope spectrum of PSK and QAM signals are analyzed. Then the recognition scheme based on the ratio of spectral peak to spectral centroid (RSPSC) of envelope spectrum is presented. Such scheme achieves a good recognition performance in the presence of ISI. Spectral centroid is utilized to enhance the robustness to noises. By extracting the envelope spectrum, in addition, the method avoids being affected by carrier frequency offset, phase offset and timing delay. The feasibility of the proposed method has been proved in the respects of theory and simulations.

Considering that the text information might be leaked through electromagnetic radiation from a computer display, a novel algorithm has been developed to detect the text information leakage. Motivated by the observation that the ‘text ‐ space – text’ characteristic for electromagnetic radiation signal contains the text information, the authors proposed a method to describe this characteristic. In this method, sparse decomposition in wavelet was used and sub‐band sparsity was defined. Variance mean ratio and correlation coefficients of sub‐band sparsity were combined as the features of electromagnetic radiation signals. By using this method, the authors can accurately and efficiently detect the text information leakage in electromagnetic radiation from a computer display without reconstructing the displayed image.

Electromagnetic radiation signal from computer display can be seen as a computer security risk if the radiation signal is intercepted and reconstructed. Electromagnetic radiation signal from computer display can also be called video leaking signal. Synchronising information extraction is the key problem of computer video leaking signal interception and reconstruction. To solve such problem, a novel synchronising information extraction algorithm based on spectral centroid has been developed. This study not only introduced spectral centroid into video leaking signal processing but also defined the concept of segmented spectral centroid. In addition, the uniformity degree of spectral centroid spacing distribution was defined to describe the harmonic characteristics of video leaking signal spectrum. The proposed algorithm can extract the electromagnetic radiation signal's synchronising information automatically and efficiently even with interference signal. Thus, the interception and reconstruction of electromagnetic radiation can be realised more effectively and the anti‐interference performance can be improved.

A Polarized OFDM (POFDM) scheme is proposed for the wireless depolarized channel. Through such scheme, each sub-carrier's polarization state, amplitude and phase can be modulated to bear information, and the spectrum efficiency can be further improved. Consider the multi-path fading channel, the channel caused depolarization impairments to POFDM are quantitatively analyzed; to mitigate such impairments, a polarization constellation compensation algorithms is also presented. Furthermore, based on the proposed compensation algorithm, the POFDM's symbol error rate is derived, then the link spectrum efficiency optimization model is established. Through calculation, the optimal modulation ratio is found to maximize POFDM's spectrum efficiency. Finally, both numerical simulations and experimental results show that POFDM can significantly improve the link spectrum efficiency compared with the traditional OFDM scheme.

Video leaking signal detection is an important part of TEMPEST, which is the technologies research focus on the investigations and studies of compromising emanations. A novel video leaking signal detection algorithm based on spectral centroid has been developed. Using the property that spectral centroid can accurately identify the signal energy center in frequency domain, the proposed algorithm can detect the display video leaking signal automatically. The uniformity degree of spectral centroid spacing distribution is defined to distinguish the video leaking signal and clutter signal automatically. Furthermore, the ant-jamming and ant-noise performance of the developed algorithm is analyzed in a practical attack scenario.

A Joint Polarization-Amplitude-Phase Modulation (JPAPM) scheme in wireless communication is proposed to improve the Power Amplifier (PA) energy efficiency. The proposed scheme introduces the signal's Polarization State (PS), amplitude and phase as the information-bearing parameters. Thus, the data rate can be further enhanced on the basis of the traditional amplitude-phase modulation. Also, since the transmitted signal's PS completely manipulated by orthogonally dual-polarized antennas is unaffected by the PA, JPAPM can let PA work in its nonlinear region to acquire high PA conversion efficiency. Furthermore, to mitigate the polarization-based impairment to JPAPM caused by the wireless channel's polarization dependent loss effect, the optimal pre-compensation algorithm is also presented. Simulation under the same symbol error rate and channel state shows the JPAPM can improve the PA energy efficiency significantly compared with the traditional quadrature amplitude modulation.

A Polarization Mode Dispersion Tolerant Subcarrier-power Allocation (PMDTSA) scheme is proposed to improve the Power Amplifier (PA) energy efficiency. The proposed scheme is applied in the multiuser downlink system based on Joint Polarization-Amplitude-Phase Modulation (JPAPM). Dut to the wireless channel's polarization mode dispersion effect, the polarization based impairment to JPAPM on each subcarrier will be diverse. Through the optimal subcarrier-power allocation, such diversity can be utilized to make PA energy efficiency optimal under the constraint of each user terminal's data rate demand. To reduce the allocation's computationally complexity, the PMDTSA scheme performs the subcarrier allocation and power allocation separately. Firstly, assuming the equal power distribution, the subcarrier is allocated through Particle Swarm Optimization (PSO); then based the obtained subcarrier allocation method, a two-step allocation algorithm is presented to distribute the PA input power on each subcarrier. Through numerical calculation, the optimal parameters setting of PSO is examined. Our results show that the proposed PMDTSA scheme is able to achieve significant improvement in PA energy efficiency.

An Energy Efficient Subcarrier-power Allocation (EESA) scheme is proposed to improve the Power Amplifier (PA) energy efficiency. The proposed scheme is applied for the Polarization-Amplitude-Phase Modulation (PAPM) in the channel with Polarization Mode Dispersion (PMD). Considering a multiuser downlink system, utilizing the diversity of the polarization characteristic on each subcarrier caused by PMD, EESA scheme can make PA energy efficiency optimal under the constraint of each user terminal's data demand. To reduce the allocation's computationally complexity, the EESA scheme performs the subcarrier allocation and power allocation separately. Firstly, by assuming the equal power distribution, the subcarrier is allocated through Differential Evolution (DE); then based the obtained subcarrier allocation method, a two-step allocation algorithm is presented to distribute the PA input power on each subcarrier. Through numerical calculation, the optimal parameters setting of DE is examined. Our results show that the EESA scheme is able to achieve significant improvement in PA energy efficiency.

A Virtual Polarization Detection (VPD) method based on the vector signal processing, is presented in this paper for the effective spectrum sensing of cognitive radios. The purpose of such VPD method is to exploit the orthogonal polarization component of primary signals besides the conjugate, in terms of vector information elements of signals. The spectrum sensing scenario of a single secondary user and multiple primary users is discussed, where the vector signals arrived at the secondary user are received by a pair of orthogonally polarized antennae. For the test of two-class problem (the primary user present class versus absent class), the secondary user optimizes the receiving polarization state to get the maximum primary signal to noise ratio by processing the received orthogonal polarization components. Specifically, it takes place in the processor of the secondary user virtually instead of antennae devices adaptation. The VPD method does not require any prior knowledge of the signal polarization states. The performance of our VPD method is compared with that of energy detection which uses scalar amplitude information only to sense the primary users. Simulation results show that the VPD method improves the spectrum sensing performance significantly.