14,346 research outputs found
Learning Self-Awareness Models for Physical Layer Security in Cognitive and AI-enabled Radios
No full textPhd thesisCognitive Radio (CR) is a paradigm shift in wireless communications to resolve the spectrum scarcity issue with the ability to self-organize, self-plan and self-regulate. On the other hand, wireless devices that can learn from their environment can also be taught things by malicious elements of their environment, and hence, malicious attacks are a great concern in the CR, especially for physical layer security. This thesis introduces a data-driven Self Awareness (SA) module in CR that can support the system to establish secure networks against various attacks from malicious users. Such users can manipulate the radio spectrum to make the CR learn wrong behaviours and take mistaken actions. The SA module consists of several functionalities that allow the radio to learn a hierarchical representation of the environment and grow its long-term memory incrementally. Therefore, this novel SA module is a way forward towards realizing the original vision of CR (i.e. Mitola’s Radio) and AI-enabled radios. This thesis starts with a basic SA module implemented in two applications, namely the CR-based IoT and CR-based mmWave. The two applications differ in the data dimensionality (high and low) and the PHY-layer level at which the SA module is implemented. Choosing an appropriate learning algorithm for each application is crucial to achieving good performance. To this purpose, several generative models such as Generative Adversarial Networks, Variational AutoEncoders and Dynamic Bayesian Networks, and unsupervised machine learning algorithms such as Self Organizing Maps Growing Neural Gas with different configurations are proposed, and their performances are analysed. In addition, we studied the integration of CR and UAVs from the physical layer security perspective. It is shown how the acquired knowledge from previous experience within the Bayesian Filtering facilitates the radio spectrum perception and allows the UAV to detect any jamming attacks immediately. Moreover, exploiting the generalized errors during abnormal situations permits characterizing and identifying the jammer at multiple levels and learning a dynamic model that embeds its dynamic behaviour. Besides, a proactive consequence can be drawn after estimating the jammer’s signal to act efficiently by mitigating its effects on the received stimuli or by designing an efficient resource allocation for anti-jamming using Active Inference. Experimental results show that introducing the novel SA functionalities provides the high accuracy of characterizing, detecting, classifying and predicting the jammer’s activities and outperforms conventional detection methods such as Energy detectors and advanced classification methods such as Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and Stacked Autoencoder (SAE). It also verifies that the proposed approach achieves a higher degree of explainability than deep learning techniques and verifies the capability to learn an efficient strategy to avoid future attacks with higher convergence speed compared to conventional Frequency Hopping and Q-learning
Self-Supervised Path Planning in UAV-Aided Wireless Networks Based on Active Inference
No full textThis paper presents a novel self-supervised path-planning method for UAV-aided networks. First, we employed an optimizer to solve training examples offline and then used the resulting solutions as demonstrations from which the UAV can learn the world model to understand the environment and implicitly discover the optimizer’s policy. UAV equipped with the world model can make real-time autonomous decisions and engage in online planning using active inference. During planning, UAV can score different policies based on the expected surprise, allowing it to choose among alternative futures. Additionally, UAV can anticipate the outcomes of its actions using the world model and assess the expected surprise in a self-supervised manner. Our method enables quicker adaptation to new situations and better performance than traditional RL, leading to broader generalizability
Probabilistic anomaly detection methods using learned models from time-series data for multimedia self-aware systems
No full textAnomaly detection techniques constitute a fundamental resource in many applications such as medical image analysis, fraud detection or video surveillance. These techniques represent an essential step also for artificial self-aware systems that can continually learn from new situations. In this chapter, we present a semisupervised method for the detection of anomalies for this type of self-aware agents. The described method leverages the message-passing capability of Generalized Dynamic Bayesian Networks (GDBNs) to provide anomalies at different abstraction levels for diverse types of time-series data (i.e., both low-dimensional and high-dimensional). The detected anomalies could consequently be employed to enable the system to evolve by integrating the new acquired knowledge. To present a case study for the description of the anomaly detection method, we propose to use multisensory data from a semiautonomous vehicle performing different tasks in a closed environment
Interactive Bayesian Generative Models for Abnormality Detection in Vehicular Networks
No full textThe following paper proposes a novel Vehicle-to-Everything (V2X) network abnormality detection scheme based on Bayesian generative models for enhanced network self-awareness functionality at the Base station (BS). In the learning phase, multi-modal data signals contrived by the vehicles' integrated and sensing module are imbued into data-driven Generalized Dynamic Bayesian network (GDBN) models. Following that, during the testing phase, an Interactive Modified Markov Jump Particle filter (IM-MJPF) is utilized to forecast forthcoming network states and vehicle trajectories by leveraging the assimilated semantics embedded in the coupled multi-GDBNs. This approach involves learning statistically correlated association between evolving trajectories and network communication links. Security and surveillance of Internet of Vehicles (IOVs) links are performed online with high detection probabilities by matching predicted with observed network connectivity maps (graphs)
A Goal-Directed Trajectory Planning Using Active Inference in UAV-Assisted Wireless Networks
No full textDeploying unmanned aerial vehicles (UAVs) as aerial base stations is an exceptional approach to reinforce terrestrial infrastructure owing to their remarkable flexibility and superior agility. However, it is essential to design their flight trajectory effectively to make the most of UAV-assisted wireless communications. This paper presents a novel method for improving wireless connectivity between UAVs and terrestrial users through effective path planning. This is achieved by developing a goal-directed trajectory planning method using active inference. First, we create a global dictionary using traveling salesman problem with profits (TSPWP) instances executed on various training examples. This dictionary represents the world model and contains letters representing available hotspots, tokens representing local paths, and words depicting complete trajectories and hotspot order. By using this world model, the UAV can understand the TSPWP’s decision-making grammar and how to use the available letters to form tokens and words at various levels of abstraction and time scales. With this knowledge, the UAV can assess encountered situations and deduce optimal routes based on the belief encoded in the world model. Our proposed method outperforms traditional Q-learning by providing fast, stable, and reliable solutions with good generalization ability
Incremental Learning through Probabilistic Behavior Prediction
No full textProceedings of 2022 30th European Signal Processing Conference (EUSIPCO), 29 Aug. - 2 Sep. 2022, Belgrade, SerbiaLearning from expert demonstrations effectively reduces the number of interactions required to train a policy between the learning agent and its environment. Where agent-environment interactions can be costly, reinforcement learning is critical, and imitation learning suffers significantly from learning hierarchical policies when the imitative agent encounters an unobserved state by the expert agent. We propose a probabilistic incremental imitation learning method that employs a Dynamic Bayesian Network to encode observed teaching-agent's behaviors. The presented model grows and matures based on imitation loss formulation at a discrete level in the online learning procedure. The learning agent is trained by using long-term predictions from the generative learning model to replicate the teacher's motion while learning how to choose an appropriate action through new experiences. Our results affirm that a Dynamic Bayesian optimal approach provides a principled framework and outperforms conventional reinforcement learning methods
Learning a Switching Bayesian Model for Jammer Detection in the Cognitive-Radio-Based Internet of Things
No full textSmart Jammer Detection for Self-Aware Cognitive UAV Radios
No full textCellular connectivity for a massive number of Unmanned Aerial Vehicles (UAVs) will overcrowd the radio spectrum and cause spectrum scarcity. Incorporating Cognitive Radio (CR) with UAVs (Cognitive-UAV-Radios) has been proposed to overcome such an issue. However, the broadcasting nature of CR and the dominant line-of-sight links of UAV makes the Cognitive-UAV-Radios susceptible to jamming attacks. In this paper, we propose a framework to detect smart jammer, which locates and attacks the UAV commands with low Jamming-to-Signal-Power-Ratio (JSR). Smart jammer is more challenging than the types of jammers that always require high power values. Our work focuses on learning a Dynamic Bayesian Network (DBN) to model and analyze the signals' behaviour statistically. A Markov Jump Particle Filter (MJPF) is employed to perform predictions and consequently detect jamming signals. The results are satisfactory in terms of detection probability and false alarm rate that outperform the conventional Energy Detector approach
Deep Learning for Spectrum Anomaly Detection in Cognitive mmWave Radios
No full textMillimeter 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
The Complete Muhammad Ali
No full textIncluding material and photographs not included in most of the 100 other books about the champion, Ishmael Reed's The Complete Muhammad Ali is more than just a biography-it is a fascinating portrait of the 20th century and the beginning of the 21st. An honest, balanced portrayal of Ali, the book includes voices that have been omitted from other books. It charts Ali's evolution from Black Nationalism to a universalism, but does not discount the Nation of Islam and Black Nationalism's important influence on his intellectual development. Filipino American author Emil Guillermo speaks about how "The Thrilla' In Manila" brought the Philippines into the 20th century. Fans of Muhammad Ali, boxing fans, and those interested in modern African American history and the Nation of Islam will be fascinated by this biography by an accomplished American author.Intro -- DEDICATION -- INTRODUCTION -- The Curious History of an Icon -- CHAPTER 1 -- CHAPTER 2 -- CHAPTER 3 -- CHAPTER 4 -- CHAPTER 5 -- CHAPTER 6 -- CHAPTER 7 -- Did the Secret Government Fear a U.S. Muslim/Overseas Muslim Alliance? -- CHAPTER 8 -- CHAPTER 9 -- The Break Between the Prophet and his Disciple -- CHAPTER 10 -- CHAPTER 11 -- CHAPTER 12 -- The GOAT (Greatest Of All Time): Ali or Louis? -- CHAPTER 13 -- The Nation of Islam, the Mob, Showdowns in Canada and Sonny Liston -- CHAPTER 14 -- CHAPTER 15 -- The Taunts: Marketing or Racism? -- CHAPTER 16 -- CHAPTER 17 -- CHAPTER 18 -- CHAPTER 19 -- Boxing and the Brain -- CHAPTER 20 -- Ali's Feet -- CHAPTER 21 -- Mr. Dick -- CHAPTER 22 -- CHAPTER 23 -- The Opening Ceremonies, November 2005 -- CHAPTER 24 -- December 2005, Las Vegas -- CHAPTER 25 -- CHAPTER 26 -- June 16, 2004 -- CHAPTER 27 -- CHAPTER 28 -- CHAPTER 29 -- Aix-en-Provence -- CHAPTER 30 -- Ali as a Black Nationalist -- San Francisco, January 2004 Black Liberation Book Fair -- CHAPTER 31 -- January 31, 2004 -- CHAPTER 32 -- October 2005, Chicago -- CHAPTER 33 -- Why Ali remained with Elijah instead of following Malcolm -- CHAPTER 34 -- CHAPTER 35 -- February 4, 2006, Oakland, California -- CHAPTER 36 -- Like Zeus Descending from Mount Olympus -- CHAPTER 37 -- CHAPTER 38 -- Tuesday, February 28, 2006, New York -- CHAPTER 39 -- Bigger Than Boxing -- CHAPTER 40 -- Tribes Gallery, New York, April 2006 -- CHAPTER 41 -- June 2006, Louisville, Kentucky -- CHAPTER 42 -- CHAPTER 43 -- CHAPTER 45 -- Bad Company -- CHAPTER 46 -- Coxson, A Very Charming Rogue -- CHAPTER 47 -- Ali and the largest embezzlement scheme in Wells Fargo history -- CHAPTER 48 -- CHAPTER 49 -- "Lonnie is a stabilizing force."-Harry Belafonte -- October 29, 2006 -- CHAPTER 50 -- Abdul Rahman -- CHAPTER 51 -- CHAPTER 52 -- CHAPTER 53How Will Ali Be Remembered? New York, January 8, 2005 -- CHAPTER 54 -- CONCLUSION -- AFTERWORD -- Boxers' Rights? -- BIBLIOGRAPHY -- MUHAMMAD ALI -- ISLAM AND NATION OF ISLAM -- BOXING -- RELATED SUBJECTS -- ALSO AVAILABLE FROM BARAKA BOOKSIncluding material and photographs not included in most of the 100 other books about the champion, Ishmael Reed's The Complete Muhammad Ali is more than just a biography-it is a fascinating portrait of the 20th century and the beginning of the 21st. An honest, balanced portrayal of Ali, the book includes voices that have been omitted from other books. It charts Ali's evolution from Black Nationalism to a universalism, but does not discount the Nation of Islam and Black Nationalism's important influence on his intellectual development. Filipino American author Emil Guillermo speaks about how "The Thrilla' In Manila" brought the Philippines into the 20th century. Fans of Muhammad Ali, boxing fans, and those interested in modern African American history and the Nation of Islam will be fascinated by this biography by an accomplished American author.Description based on publisher supplied metadata and other sources.Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, YYYY. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries
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