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Securing MIMO Wiretap Channel With Learning-Based Friendly Jamming Under Imperfect CSI

IEEE Internet of Things Journal 2025年第11期12卷 16009-16022页

作者： Minh Tuan, Bui Nguyen, Diep N. Linh Trung, Nguyen Nguyen, Van-Dinh Van Huynh, Nguyen Thai Hoang, Dinh Krunz, Marwan Dutkiewicz, Eryk University of Technology Sydney School of Electrical and Data Engineering SydneyNSW2007 Australia University of Engineering and Technology Vietnam National University Faculty of Electronics and Telecommunications Hanoi100000 Viet Nam College of Engineering and Computer Science VinUniversity Hanoi100000 Viet Nam University of Liverpool Department of Electrical Engineering and Electronics LiverpoolL69 3GJ United Kingdom The University of Arizona Department of Electrical and Computer Engineering TucsonAZ85721 United States

Wireless communications are particularly vulnerable to eavesdropping attacks due to their broadcast nature. To effectively deal with eavesdroppers, existing security techniques usually require accurate channel state information (CSI), e.g., for friendly jamming (FJ), and/or additional computing resources at transceivers, e.g., cryptography-based solutions, which unfortunately may not be feasible in practice. This challenge is even more acute in low-end IoT devices. We thus introduce a novel deep learning-based FJ framework that can effectively defeat eavesdropping attacks with imperfect CSI and even without CSI of legitimate channels. In particular, we first develop an autoencoder-based communication architecture with FJ, namely, AEFJ, to jointly maximize the secrecy rate and minimize the block error rate (BLER) at the receiver without requiring perfect CSI of the legitimate channels. In addition, to deal with the case without CSI, we leverage the mutual information neural estimation (MINE) concept and design a MINE-based FJ scheme that can achieve comparable security performance to the conventional FJ methods that require perfect CSI. Extensive simulations in a multiple-input-multiple-output (MIMO) system demonstrate that our proposed solution can effectively deal with eavesdropping attacks in various settings. Moreover, the proposed framework can seamlessly integrate MIMO security and detection tasks into a unified end-to-end learning process. This integrated approach can significantly maximize the throughput and minimize the BLER, offering a good solution for enhancing communication security in wireless communication systems. © 2014 IEEE.

关键词： Jamming

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Design of PID Droop Control for Fast Power Sharing in Inverter-Based Microgrid

Design of PID Droop Control for Fast Power Sharing in Invert...

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2024 Energy Conversion Congress and Expo Europe, ECCE Europe 2024

作者： Nazeri, Ahmad Ali Friebe, Jens Zacharias, Peter Faculty of Electrical Engineering Computer Science University of Kassel Department of Power Electronics Kassel Germany

ISBN: (纸本)9798350364446

Microgrids (MGs) serve as central interfaces for distributed generation, predominantly employing voltage source inverters (VSIs). These MGs operate in either autonomous or grid-connected modes, facilitating local power generation and storage. However, autonomous MGs, lacking grid support, are susceptible to instability, particularly with multiple generators sharing power. This paper addresses instability challenges in VSI-based MGs through comprehensive system modeling, analysis, and design. A novel approach is proposed, employing a modified virtual inertia PID droop control with virtual impedance, facilitates swift response to frequency and voltage fluctuations, ensuring precise power allocation among paralleled VSIs. Moreover, this paper presents the small signal analysis of the PID droop control and outlines the secondary control design, encompassing DDSRF-PLL, frequency, and voltage restoration loops. System validation is conducted through MATLAB/Simulink modeling and experimental verification on a hardware-in-the-loop (HiL) testbench, confirming the efficacy of the proposed methodologies in low inertia MGs. © 2024 IEEE.

关键词： MATLAB

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Modular Design of Solid-State Transformer with High Flexibility

Modular Design of Solid-State Transformer with High Flexibil...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Ma, Zhuxuan Cao, Hui Zhao, Yue University of Arkansas Dept. of Electrical Engineering and Computer Science FayettevilleAR United States

ISBN: (纸本)9798350376067

This paper presents a modular solid state transformer (SST) design incorporating dual active bridge and cascaded H-Bridge topologies to enhance electric vehicle (EV) charging infrastructure. By using modular design concept for the power cell hardware, the proposed SST can be suitable for industrial usage with low manufacturing cost, and low maintenance complexity. With various combinations of the power cells, the whole SST converter has high flexibility for the power scale and circuit topology. This guarantees the fast installation and high transportability of the SST, suitable for the high-demanded EV charging applications. The design detail of the busbar used for the SST has been presented, which shows how to reduce the stray inductance of the busbar while keeping its high current conduction ability and high insulation. To validate the high current conduction ability of the busbar, finite element analysis is conducted to assess busbar current density and temperature. Finally, an advanced control strategy is introduced to effectively reduce DC link ripple, eliminating the need for bulky components. © 2024 IEEE.

关键词： Busbars

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Player Position Binary Classification Model 3

Player Position Binary Classification Model

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3rd International Conference on computer Applications Technology, CCAT 2024

作者： Morgan, Kayanna A. Godasu, Rajesh Grant, Emanuel S. University of North Dakota School of Electrical Engineering and Computer Science Grand Forks United States

ISBN: (纸本)9798331539795

This paper presents a binary classification model aimed at predicting whether a football player is best suited for an offensive or defensive position based on their skill set. The study addresses the challenges in precisely projecting a player's future potential solely from their skills, a gap in sports analytics and player evaluation research. Leveraging machine learning techniques, specifically neural networks, the model aims to classify players into their optimal position groups, contributing to talent identification and recruitment strategies in football. Evaluation metrics demonstrate the model's ability to accurately predict player positions, with a slight improvement observed in the enhanced model. However, the increase in model complexity does not yield significant performance gains, emphasizing the importance of balancing complexity with performance improvement. © 2024 IEEE.

关键词： Adversarial machine learning

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Data Poisoning Against Federated Learning: Comparative Analysis Under Label-Flipping Attacks and GAN-Generated EEG Data 2

Data Poisoning Against Federated Learning: Comparative Analy...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Alsereidi, Maryam Awadallah, Abeer Alkaabi, Alreem Yoon, Sangyoung Yeun, Chan Yeob Khalifa University Electrical Engineering And Computer Science Department Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350394962

Federated Learning (FL) has emerged as a privacy-preserving machine learning approach, enabling collaborative model training across devices while maintaining the decentralization of raw data. This paper investigates the application of FL in insider threat detection, a critical aspect of organizational security that addresses potential risks posed by individuals with access to sensitive data. We focus on using Electroencephalogram (EEG) data to identify malicious intentions, which consists of highly sensitive brain signals that we aim to safeguard by employing FL. Despite its advantages, FL still encounters a rising threat from data poisoning attacks. This study investigates the resilience of our FL model against label-flipping attacks, utilizing three classifiers: Multiplayer Perceptron (MLP), Convolutional Neural Network (CNN), and an ensemble learning classifier, the Voting Classifier (VC). Due to insufficient EEG data, a Generative Adversarial Network (GAN) model is utilized to augment and increase the size of the data. Our findings reveal that VC demonstrates the highest performance with an accuracy of 95% for the original dataset. In contrast, CNN is the sole classifier that outperformed others with the GAN-generated dataset, achieving an accuracy of 93.5%. Furthermore, we examine various cases and scenarios of label-flipping, demonstrating that compromising one client (device) in an FL framework has the least overall performance degradation on the model, emphasizing the efficacy of FL in fostering collaborative learning. © 2024 IEEE.

关键词： Generative adversarial networks

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A Review on Partial Discharge Detection Techniques in High Voltage Rotating Machines 50

A Review on Partial Discharge Detection Techniques in High V...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Refaat, Shady S. Saleh, Mohammad AlShaikh Kameli, Sayed Mohammad Department of Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar School of Physics Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom Department of Electrical and Computer Engineering Texas A&M University College Station United States

ISBN: (纸本)9781665464543

Monitoring partial discharges (PDs) in high voltage (HV) electrical equipment is an effective tool to prevent unscheduled shutdown and eliminate possible failures. The inception of PDs can take various forms within the insulation system and can be detected using established approaches. The most common cause of HV rotating machine failure is the initiation of PDs in the stator winding insulation system. Various online and offline techniques have been proposed to monitor, detect, diagnose, and locate PDs in HV rotating machines. This paper gives an overview of existing PD detection and localization technologies in HV rotating machines. A comparison of online and offline PD detection approaches is discussed in this paper. Finally, a brief review of the methods of PD classification is provided. © 2024 IEEE.

关键词： Machine windings

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Predictive Analysis of Sarajevo's AQI using Machine Learning Models for Varied Data Granularity and Prediction Windows 47

Predictive Analysis of Sarajevo's AQI using Machine Learning...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Zolota, Emina Hasić, Vahidin Mević, Amina Delić, Amra Krivić, Senka University Of Sarajevo Faculty Of Electrical Engineering Department For Computer Science And Informatics Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9798350382495

This study scrutinizes five years of Sarajevo's Air Quality Index (AQI) data using diverse machine learning models - Fourier autoregressive integrated moving average (Fourier ARIMA), Prophet, and Long short-term memory (LSTM) - to forecast AQI levels. Focusing on various prediction frames, we evaluate model performances and identify optimal strategies for different temporal granularities. Our research unveils subtle insights into each model's efficacy, shedding light on their strengths and limitations in predicting AQI across varied timeframes. This research presents a robust framework for automatic optimization of AQI predictions, emphasizing the influence of temporal granularity on prediction accuracy, automatically selecting the most efficient models and parameters. These insights hold significant implications for data-driven decision-making in urban air quality control, paving the way for proactive and targeted interventions to improve air quality in Sarajevo and similar urban environments. © 2024 IEEE.

关键词： Air quality

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Data-driven Network-aware Control of Thermostatically Controlled Loads with Unknown Dynamics 63

Data-driven Network-aware Control of Thermostatically Contro...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Jang, Sunho Mathieu, Johanna L. Ozay, Necmiye University of Michigan Department of Electrical Engineering and Computer Science Ann ArborMI United States

ISBN: (纸本)9798350316339

This paper presents a novel data-driven control algorithm that coordinates a large aggregation of heterogeneous thermostatically controlled loads (TCLs) with unknown temperature dynamics and disturbance distributions to provide balancing services to the grid. Each TCL is subject to local constraints on their temperatures due to user comfort and global constraints on the number of TCLs in a mode, called mode-counting constraints, for the safe operation of the distribution network. We first develop a data-driven method to compute sets of modes at each discretized interval of the temperature state space in which the probability of moving outside of the temperature dead-band at the next step is bounded at a certain confidence level. Subsequently, we design a model predictive control (MPC) algorithm that instructs every TCL to switch to the modes within the obtained set while satisfying the mode-counting constraints. A case study compares the proposed control algorithm with a benchmark and verifies its effectiveness in maintaining end-user comfort and the safe operation of the distribution network. © 2024 IEEE.

关键词： Predictive control systems

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Light Pollution Caused by Railway Station Lighting Systems - Case Study

Light Pollution Caused by Railway Station Lighting Systems -...

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2024 IEEE Sustainable Smart Lighting World Conference and Expo, LS24 2024

作者： Houzva, Martin Novak, Tomas Valicek, Pavel VSB - Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Ostrava Czech Republic

ISBN: (纸本)9798331540197

This paper deals with the radiation of outdoor lighting systems on railways. Due to the number and size of railway stations and their areas, these can contribute significantly to the increase of "light pollution"through the radiation of their luminous flux into the upper hemisphere. Outdoor lighting systems on railways behave de-facto like road lighting systems, but they are usually not taken into account as a significant contributor to light pollution and are of little *** aim of the paper is to show, by the example of the behaviour of a small railway station, the changes in the behaviour of the luminous flux in a given small railway station. The findings address whether new LED-based lighting systems, which respect all normative recommendations not only in terms of the required light technical parameters in the visual task areas, but especially in terms of the requirements for minimizing direct radiation into the upper hemisphere, are really better than the physically and morally obsolete lighting systems implemented 20 to 40 years ago, dominated by luminaires equipped with high-pressure sodium lamps. The paper discusses not only the light technical parameters, but also the power input parameters and the number of luminaires that are necessary to achieve the normative requirements, which result not from environmental requirements, but from safety requirements. © 2024 IEEE.

关键词： Railroads

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Requirements Elicitation for Machine Learning Applications: A Research Preview

Requirements Elicitation for Machine Learning Applications: ...

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2024 IEEE International Conferences of AI, science, engineering, and Technology, AIxSET 2024

作者： Elvira, Timothy Procko, Tyler Thomas Ochoa, Omar Embry-Riddle Aeronautical University Department of Electrical Engineering and Computer Science Florida United States

ISBN: (纸本)9798350390995

The development of software systems is preceded by an important first phase, requirements elicitation, wherein developers establish the intended functionality of a system to be developed in a series of interviews with a customer. These requirements can often be raw, requiring refinement in an iterative process. Traditional software requirements are of the form 'The system shall... ', where each requirement is written to be clear, concise, consistent, complete, testable and traceable through development. Because Machine Learning (ML) is stochastic, or uncertain, in the face of unseen data, ML behavior cannot be precisely defined. As such, it is posited that software requirements specific to ML must delineate a window of acceptable behavior, e.g., a plus/minus value for evaluated model metrics. The provision of such is essential for the quality of future ML software systems, as the current trend of large-scale generative ML has brought about a paradigm of fine-tuning, e.g., of Large Language Models, in which pre-trained large models are taken 'off the shelf' and made fit for very specific purposes. These specific purposes vary with different use cases, but they are effectively undeclared requirements. The present paper discusses requirements elicitation in the context of ML by considering the four-software engineering requirement elicitation techniques: conversational, observational, analytic and synthetic, providing future researchers with insight for eliciting quality ML requirements. © 2024 IEEE.

关键词： Stochastic systems

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