Instant messaging applications (IM apps) are among the most frequent sources of signalling storms in mobile networks. Poorly designed IM apps can be exploited by adversaries to initiate stealthy signalling storm attacks (SSAs). Recent advancements in the 5G Radio Resource Control (RRC) protocol have introduced a novel attack vector, where malicious actors manipulate the 5G inactivity timer to generate excessive signalling messages. Due to the inherent complexity of the working mechanism of the IM app and the diversity of user behavior, distinguishing between legitimate and malicious IM signalling traffic is a challenge. In this study, a comprehensive source traffic model (STM) is presented that captures the operational dynamics of modern IM applications alongside a wide spectrum of user interaction patterns. This model enables a deeper understanding of normal IM traffic and its corresponding signalling load. Building upon this foundation, we introduce a threat model that demonstrates how attackers can emulate normal user behavior and exploit IM app functionalities to launch inactivity timer-based SSAs in 5G networks. To detect such attacks, two novel traffic analysis features: Burst Correlation Index (BCI) and Kolmogorov–Smirnov Distance Contrast (KSDC) are proposed. These metrics effectively highlight subtle differences between benign and malicious traffic patterns, enhancing classification accuracy significantly.

This research paper delves into interference mitigation within Low Earth Orbit (LEO) satellite constellations, particularly when operating under constraints of limited radio environment information. Leveraging cognitive capabilities facilitated by the Radio Environment Map (REM), we explore strategies to mitigate the impact of both intentional and unintentional interference using planar antenna array (PAA) beamforming techniques. We address the complexities encountered in the design of beamforming weights, a challenge exacerbated by the array size and the increasing number of directions of interest and avoidance. Furthermore, we conduct an extensive analysis of beamforming performance from various perspectives associated with limited REM information: static versus dynamic, partial versus full, and perfect versus imperfect. To substantiate our findings, we provide simulation results and offer conclusions based on the outcomes of our investigation.

Transmitter authentication is critical for secured Internet of Things (IoT) applications. Recently, there has been growing interest in utilizing the physical layer authentication technique, radio frequency (RF) fingerprinting, to introduce extra security measurements without adding additional components. This work presents a novel fingerprint exploitation modality, Density Trace Plot (DTP), to leverage RF fingerprints originating from symbol transition trajectories for transmitter authentication. With a particular focus on IQ imbalance as the source impairment for RF fingerprints, we investigate the feasibility of three types of DTPs based on constellation, eye, and phase traces. The potential fingerprints presented in the DTP modalities are then used in training three deep learning classifiers: 2D-convolutional neural network (CNN), 2D-CNN+bi-directional long short-term memory (biLSTM), and 3D-CNN for transmitter authentication. The feasibility of the proposed approach in both wired and wireless conditions is validated using an experimental setup built using ADALM-PLUTO software-defined radios (SDRs). Experimental results demonstrate the best authentication accuracy of 96.7% is achieved across signals of various modulation complexities.

Spectrum scarcity has surfaced as a prominent concern in wireless radio communications with the emergence of new technologies over the past few years. As a result, there is a growing need for better understanding of the spectrum occupancy with newly emerging access technologies supporting the Internet of Things. In this article, we present a framework to capture and model the traffic behavior of short-time spectrum occupancy for Internet-of-Things (IoT) applications in the shared bands to determine the existing interference. The proposed capturing method utilizes a software-defined radio to monitor the short bursts of IoT transmissions by capturing the time-series data which is converted to power spectral density to extract the observed occupancy. Furthermore, we propose the use of an unsupervised machine learning technique to enhance conventionally implemented energy detection methods. Our experimental results show that the temporal and frequency behavior of the spectrum can be well captured using the combination of two models, namely, semi-Markov chains and a Poisson-distribution arrival rate. We conduct an extensive measurement campaign in different urban environments and incorporate the spatial effect on the IoT shared spectrum.

Hidden Markov Chains (HMCs) and, more recently, Hidden semi-Markov Chains (HsMCs) have been used by several groups of researchers to provide a model for indoor localization. A homogeneous HMC is completely determined by the state initial probability vector and the state transition probability matrix. This is also true for the HsMC provided the state duration probability is given. These parameters are often chosen heuristically but when sufficient measurement training data are available, they can be learned using the well-known Baum-Welch algorithm. Given the model parameters, approaches such as the forward-only algorithm, the forward-backwards algorithm and the Viterbi algorithm can be applied for state sequence inference under the HMC/HsMC framework. In indoor localization applications, there is often insufficient prior information to specify such parameters in advance of the application and they have to be learned from limited amounts of training data. In this paper, we endeavour to evaluate the parameter learning accuracy of the Baum-Welch algorithm using varying amounts of training data, and evaluate the influence of applying inaccurate model parameters on these typical state estimation algorithms under both the HMC and HsMC frameworks. All of the evaluations are based on received signal strength (RSS) for application to indoor localization.

This letter presents an extension of the free-space method for the dielectric characterization of Eucalypt litter fire using the measured transmission phase shift of a propagating signal over a frequency range of 5-40 GHz. This method is especially suitable for a broadband, routine, nonintrusive, and accurate evaluation of fire dielectric properties from the measured transmission coefficient (S21). A 72 cm × 47 cm rectangular brick burning area prepared with Eucalypt ground litter was equipped with four K-type thermocouples at different heights to measure flame temperatures, which ranged from 283-865 K. The measured phase shift of an electromagnetic signal transmitted through the Eucalypt fire varied from 12.2 to -166.7° at 5.35 and 39.74 GHz. From this phase shift, the calculated real part of the relative permittivity of the fire was between 0.877 and 1, with the imaginary part from 0.02 to 0.09. The relative permittivity of less than unity confirms the formation of plasma in the combustion zone of fire, and shows good agreement with previous research. However, in this letter, the combustion zone region is defined with significantly higher accuracy, and characteristics are evaluated over a much broader frequency range. The extracted parameters are important for understanding of the reaction of electromagnetic waves in wildfire environments and can be used to design radar systems for forest fire detection.

In years have seen the advent of new types of automotive antennas, such as blade or 'shark-fin' antennas and conformal planar roof mounted antennas. In many cases it is desirable to paint these antennas to improve the appearance of the vehicle. In this communication we present an investigation of the effect that both metallic and non-metallic two-pack polyurethane paint has on a structure radiating at approximately 1.5 GHz (GPS Ll-band), with a particular emphasis on the impedance bandwidth and radiation performance.