An Emergent Self-Awareness Module for Physical Layer Security in Cognitive UAV Radios

In this paper, we propose to introduce an emergent Self-Awareness (SA) module at the physical layer (PHY) in Cognitive Unmanned Aerial Vehicle (UAV) Radios to improve PHY security, especially against jamming attacks. SA is based on learning a hierarchical representation of the radio environment by means of a proposed Hierarchical Dynamic Bayesian Network (HDBN). It is shown how the acquired knowledge from previous experiences facilitate the radio spectrum perception and allow the radio to detect abnormal behaviours caused by jamming attacks. Detecting abnormalities realize a fundamental step towards growing up incrementally the radio\u2019s long-term memory. Deviations from predictions estimated during abnormal situations are used to characterize jammers at multiple levels and discover their dynamic behavioural rules. Besides, a proactive consequence can be drawn after estimating the jammer\u2019s signal to act efficiently by mitigating its effects on the received stimuli. Simulation results show that the introduction of the novel SA functionalities with the proposed HDBN framework provides the high accuracy of characterizing, detecting and predicting the jammer\u2019s activities

Jammer Detection in Vehicular V2X Networks

Vehicle-to-Everything (V2X) is an emergent technology for enhancing traffic efficiency, road safety and autonomous driving. Vehicles interconnected with their prevalent wireless environment are prone to various security threats that might affect traffic and life safety mmensely. Jamming attacks, a legacy and dated problem, still persists much to the havoc of V2X communications. The following paper proposes a framework for jammer detection adapted to V2X communications scenario. A Generalized Dynamic Bayesian network is used to learn the V2X signal environment in a statistical manner. Subsequently, a Modified Markov Jump Particle filter (M-MJPF) is used for signal predictions where the innovations in the observed signal versus the predicted signal enable our framework to detect the jammer. Simulation results highlight the efficacy and accuracy of our approach in V2X jammer detection

A Novel Resource Allocation for Anti-jamming in Cognitive-UAVs: an Active Inference Approach

This work proposes a novel resource allocation strategy for anti-jamming in Cognitive Radio using Active Inference (AIn), and a cognitive-UAV is employed as a case study. An Active Generalized Dynamic Bayesian Network (Active-GDBN) is proposed to represent the external environment that jointly encodes the physical signal dynamics and the dynamic interaction between UAV and jammer in the spectrum. We cast the action and planning as a Bayesian inference problem that can be solved by avoiding surprising states (minimizing abnormality) during online learning. Simulation results verify the effectiveness of the proposed AIn approach in minimizing abnormalities (maximizing rewards) and has a high convergence speed by comparing it with the conventional Frequency Hopping and Q-learnin

Calculations of the Optical Properties of Layered Spheroidal Models of Cosmic Dust Particles

Сфероидальная модель частиц широко используется в разных науках, и особенно в астрофизике, из-за недостаточных сведений о форме и структуре космических пылинок. Применение этой модели ограничено трудностью быстрого и достаточно аккуратного моделирования рассеяния света крупными (слоистыми) сфероидальными частицами. Мы развили новый подход к расчету оптических свойств как одиночных сфероидов, так и их ансамблей, что необходимо для рассмотрения различных проблем, включая перенос поляризованного излучения. Тестирование показало, что наш подход дает надежные результаты для сплюснутых и вытянутых слоистых сфероидов даже при дифракционном параметре x = 2πa / λ (где a и λ — размер частицы и длина волны излучения), превышающем 100.The spheroidal model of solid particles is widely applied in various sciences, and in particular in astrophysics because of a lack of knowledge of the shape and structure of cosmic dust grains. Adaptation of this model is limited by difficulties of fast and accurate simulations of light scattering by large (layered) spheroidal particles. We develop a new approach to calculations of the optical properties of both single spheroids and ensembles of spheroids, which is necessary for consideration of different problems including polarized radiative transfer. Testing has demonstrated that our approach provides reliable results for oblate and prolate layered spheroids even for the diffraction parameter x = 2πa / λ (where a and λ are the particle size and radiation wavelength, respectively) exceeding 100

Jammer detection in M-QAM-OFDM by learning a dynamic Bayesian model for the cognitive radio

Communication and information field has witnessed recent developments in wireless technologies. Among such emerging technologies, the Internet of Things (IoT) is gaining a lot of popularity and attention in almost every field. IoT devices have to be equipped with cognitive capabilities to enhance spectrum utilization by sensing and learning the surrounding environment. IoT network is susceptible to the various jamming attacks which interrupt users communication. In this paper, two systems (Single and Bank-Parallel) have been proposed to implement a Dynamic Bayesian Network (DBN) Model to detect jammer in Orthogonal Frequency Division Multiplexing (OFDM) sub-carriers modulated with different M-QAM. The comparison of the two systems has been evaluated by simulation results after analyzing the effect of self-organizing map's (SOM) size on the performance of the proposed systems in relation to M-QAM modulation

Astronomy in St. Petersburg University in XVIII—XIX centuries

Кратко рассматривается развитие астрономии в Санкт-Петербургском университете с момента его основания и до начала XX в. Отмечены важнейшие достижения университетских астрономов в эти времена.The development of astronomy in the St. Petersburg University from its foundation until the XX century is briefly considered. The most important achievements of University astronomers during those times are outlined

Integrated Sensing and Communication for Joint GPS Spoofing and Jamming Detection in Vehicular V2X Networks

Vehicle-to-everything (V2X) communication is expected to be a prominent component of the sixth generation (6G) to accomplish intelligent transportation systems (ITS). Autonomous vehicles relying only on onboard sensors cannot bypass the limitations of safety and reliability. Thus, integrated sensing and communication is proposed as an effective way to achieve high situational- and self-awareness levels, enabling V2X to perceive the physical world and adjust its behaviour to emergencies. Secure navigation through the Global Positioning System (GPS) is essential in ITS for safe operation. Nevertheless, due to the lack of encryption and authentication mechanisms of civil GPS receivers, spoofers can easily replicate satellite signals by launching GPS spoofing attacks to deceive the vehicle and manipulate navigation data. In addition, due to its shared nature, V2X links are prone to jamming attacks which might endanger vehicular safety. This paper proposes a method to jointly detect GPS spoofing and jamming attacks in a V2X network. Simulation results demonstrate that the proposed method can detect spoofers and jammers with high detection probabilities

A Novel Resource Allocation for Anti-Jamming in Cognitive-UAVs: An Active Inference Approach

This work proposes a novel resource allocation strategy for anti-jamming in Cognitive Radio using Active Inference (AIn), and a cognitive-UAV is employed as a case study. An Active Generalized Dynamic Bayesian Network (Active-GDBN) is proposed to represent the external environment that jointly encodes the physical signal dynamics and the dynamic interaction between UAV and jammer in the spectrum. We cast the action and planning as a Bayesian inference problem that can be solved by avoiding surprising states (minimizing abnormality) during online learning. Simulation results verify the effectiveness of the proposed AIn approach in minimizing abnormalities (maximizing rewards) and has a high convergence speed by comparing it with the conventional Frequency Hopping and Q-learning

Deep learning for spectrum anomaly detection in cognitive mmWave radios

Millimeter Wave (mmWave) band can be a solution to serve the vast number of Internet of Things (IoT) and Vehicle to Everything (V2X) devices. In this context, Cognitive Radio (CR) is capable of managing the mmWave spectrum sharing efficiently. However, Cognitive mmWave Radios are vulnerable to malicious users due to the complex dynamic radio environment and the shared access medium. This indicates the necessity to implement techniques able to detect precisely any anomalous behaviour in the spectrum to build secure and efficient radios. In this work, we propose a comparison framework between deep generative models: Conditional Generative Adversarial Network (C-GAN), Auxiliary Classifier Generative Adversarial Network (AC-GAN), and Variational Auto Encoder (VAE) used to detect anomalies inside the dynamic radio spectrum. For the sake of the evaluation, a real mmWave dataset is used, and results show that all of the models achieve high probability in detecting spectrum anomalies. Especially, AC-GAN that outperforms C-GAN and VAE in terms of accuracy and probability of detection