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

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

Self-Learning Bayesian Generative Models for Jammer Detection in Cognitive-UAV-Radios

Unmanned Aerial Vehicles (UAVs) attracted both industry and research community owing to their fascinating features like mobility, deployment flexibility and strong Line of Sight (LoS) links. The integration of Cognitive Radio (CR) can greatly help UAVs to overcome several issues especially spectrum scarcity. However, the dynamic radio environment in CR and the strong dependence of safe communications from LoS channels integrity in UAV communications make the Cognitive- UAV-Radio vulnerable to jamming attacks. This work aims to study the integration of CR and UAVs introducing a Self- Awareness (SA) framework from the physical layer security perspective. Under the SA framework, a Dynamic Bayesian Network (DBN) model is proposed as a representation of the radio environment and a modified Markov Jump Particle Filter (MJPF) is employed for prediction and state estimation purposes. A novel jammer detection framework is proposed that allows the UAV to perform abnormality evaluation at different hierarchical levels. The jammer is shown to be located effectively in both time and frequency domains. Experimental results show the effectiveness of the proposed framework in terms of detection probability and accuracy

Active Inference Integrated with Imitation Learning for Autonomous Driving

Classical imitation learning methods suffer substantially from the learning hierarchical policies when the imitative agent faces an unobserved state by the expert agent. To address these drawbacks, we propose an online active learning through active inference approach that encodes the expert\u2019s demonstrations based on observation-action to improve the learner\u2019s future motion prediction. For this purpose, we provide a switching Dynamic Bayesian Network based on the dynamic interaction between the expert agent and another object in its surrounding as a reference model, which we exploit to initialize an incremental probabilistic learning model. This learning model grows and matures based on the free-energy formulation and message passing of active inference dynamically at discrete and continuous levels in an online active learning phase. In this scheme, generalized states of the learning world are represented as distance-vector, where it is the learner\u2019s observation concerning its interaction with a moving object. Considering the distance vector entail intentions, it enables action prediction evaluation in a prospective sense. We illustrate these points using simulations of driving intelligent agents. The learning agent is trained by using long-term predictions from the generative learning model to reproduce the expert\u2019s motion while learning how to select a suitable action through new experiences. Our results affirm that a Dynamic Bayesian optimal approach provides a principled framework and outperforms conventional reinforcement learning methods. Furthermore, it endorses the general formulation of action prediction as active inference