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Innovative Investigation of the Resilience of EV Charging Infrastructure Under Cyber-Physical Threats Based on a Real-Time Co-Simulation Testbed

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IET Cyber-Physical Systems: Theory and Applications 2025年第1期10卷

作者： Alasali, Feras Ghalyon, Salah Abu El-Naily, Naser Abuashour, Mohammed I. AlMajali, Anas Itradat, Awni Holderbaum, William Department of Electrical Engineering Faculty of Engineering The Hashemite University Zarqa Jordan Department of Computer Engineering Faculty of Engineering The Hashemite University Zarqa Jordan College of Electrical and Electronics Technology Benghazi Libya Department of Computer Science and Engineering American University of Sharjah Sharjah United Arab Emirates School of Science Engineering & Environment University of Salford Salford United Kingdom

The rapid expansion of electric vehicle (EV) charging infrastructure has introduced significant vulnerabilities to cyber-physical threats, raising concerns about the resilience of both charging and smart power grid systems. This paper presents an innovative investigation into the resilience of EV charging infrastructure using a real-time co-simulation testbed, integrating both power network models and communication protocols such as IEC 61850. The study addresses gaps in existing research by implementing a realistic smart grid environment that incorporates EVs, charging stations and communication networks to simulate cyber-physical interactions. Key cyber-attacks, such as remote charging station status and configuration manipulations and their impact on it, are analysed in real-time simulations. Results show that even a relatively small attack utilising an IEEE 9-bus system with two EV charging stations can severely disrupt grid stability. The paper also explores various attacks targeting EV infrastructure, including charging stations, communication protocols, and management systems. The combined effects of cyber-attacks on power consumption and current variation highlight the critical importance of ensuring that charging infrastructure can adapt to sudden changes in demand while maintaining operational integrity. © 2025 The Author(s). IET Cyber-Physical Systems: Theory & Applications published by John Wiley & Sons Ltd on behalf of The Institution of engineering and technology.

关键词： Vehicle-to-grid

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Optimization of Power in Power Converters Using Machine Learning: An Overview

Optimization of Power in Power Converters Using Machine Lear...

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Sustainable Energy Technologies and Computational Intelligence (SETCOM), International Conference on

作者： Prashant Kumar Nayak Debabrata Swain Kaibalya Prasad Panda Department of Computer Science and Engineering Pandit Deendayal Energy University Gandhinagar India Department of Electrical Engineering School of Energy Technology Pandit Deendayal Energy University Gandhinagar India

ISBN: (数字)9798331520540

ISBN: (纸本)9798331520557

The rapid evolution in power electronics have brought significant attention to the optimization of power converters, which are essential for efficient energy conversion and management. Traditional techniques for optimizing power converters often struggle with the increasing complexity of modern systems, including nonlinear dynamics, diverse operating conditions, and stringent efficiency requirements. Last few decades, it has been observed that machine learning has evolved as hopeful solution with data driven methodologies to address various challenges. This survey provides the integration of machine learning for optimizing power converter, reviewing new approaches, key procedures and application in different domain. This categorizes different types of machine learning techniques and evaluate their usage in multiple fields like real-time control, fault detection, thermal management and system design. The paper also discusses different datasets, evaluation metrics and implementation framework in field of power converters. Lastly, challenges like computational complexity, data security and real-world deployment are highlighted along with direction towards future research to enhance the integration between machine learning and power converters. The comprehensive review aims to provide insight of researchers carried out to develop an intelligent power system.

关键词： Training Surveys Reviews Neural networks Machine learning Thermal management Real-time systems Power systems Nonlinear dynamical systems Optimization

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Federated Learning in Big Data with IoT for Intrusion Detection

Federated Learning in Big Data with IoT for Intrusion Detect...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Anitha Govindaram J. S. Prasath A. Suganya K. Jayasakthi N. Rajkumar Jose Anand A. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Thandalam Chennai India Department of Computer Science and Engineering Sapthagiri NPS University Chikkasandra Bengaluru Karnataka Department of Artificial Intelligence and Data Science Anand Institute of Higher Technology Chengalpattu Tamil Nadu Department of ECE KCG College of Technology Chennai Tamil Nadu Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Thiruvalluvar Tamil Nadu

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

Artificial Intelligence (AI) part of Federated Learning (FL) builds on distributed data and modelling to deliver learning to the edge of the device. Even though FL has been celebrated as the beginning of AI, it has not yet gained mainstream adoption, mostly because of concerns about privacy and security. Security and privacy issues with the FL technique need to be found, examined, and recorded before at this point, we can expect to see significant progress in this field of study or widespread adoption of the FL approach. This article aims to afford a complete review of the methods discussed with FL for huge data by introducing studies from six angles: blockchain, privacy, hybrid approach, VFL, FL, and foundation design and device heterogeneity. This survey can address the challenges of FL, offer their predictions for the future of research on the subject, outline how FL is now being put to use in the real world, and with most salient features.

关键词： Surveys Privacy Data privacy Federated learning Reviews Intrusion detection Learning (artificial intelligence) Security Informatics Mobile computing

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Exploring the role of behavioral analytics and anomaly detection in securing mobile networks for critical infrastructure

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Information Security Journal 2025年

作者： Gangadhar, Ch. Mapari, Rahul Ganpat Muthevi, Anil Kumar Suneetha, Akula B V, Santhosh Krishna Mouleswararao, B. Department of Electronics & Communication Engineering Prasad V. Potluri Siddhartha Institute of Technology Andhra Pradesh Vijayawada India Department of Electronics and Telecommunication Engineering Pimpri Chinchwad College of Engineering and Research Pune India Department of CSE Aditya University Andhra Pradesh Surampalem India Department of Computer Science and Engineering KKR & KSR Institute of Technology and Sciences Andhra Pradesh Guntur India Department of CSE New Horizon College of Engineering Karnataka Bengaluru India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India

Mobile networks have an ever-increasing presence in critical infrastructure, enabling just-in-time monitoring and real-time control in various domains, from energy and transport to healthcare. However, the security of these systems is now vulnerable to new cybersecurity threats from this integration. Evaluating the potential of behavioral analytics and anomaly detection in securing mobile networks for critical infrastructure. This paper presents a novel Multi-Layer Adaptive Anomaly Detection System (MAADS) that utilizes recent advances in machine learning (ML) to identify anomalies and detect novel threats in mobile networks. MAADS detects anomalies in mobile networks across three layers: network traffic, user mobility, and device behavior. It does this through privacy-preserving data collection, multi-model anomaly detection, and an adaptive response framework. The empirical evaluation of the proposed system shows that MAADS can detect anomalies with 95% precision and 93% recall. It is demonstrated to identify 87% of anomalies not detected by standard ML-based systems. It also maintains its performance up to 100,000 nodes and consistently performs across other critical infrastructure sectors. The analysis and comparison of MAADS with existing solutions show that it outperforms existing techniques in terms of the ability to adapt to novel threats and the explainability of the detected anomalies. © 2025 Taylor & Francis Group, LLC.

关键词： Cybersecurity

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DanmuA11y: Making Time-Synced On-Screen Video Comments (Danmu) Accessible to Blind and Low Vision Users via Multi-Viewer Audio Discussions 25

DanmuA11y: Making Time-Synced On-Screen Video Comments (Danm...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

作者： Shuchang Xu Xiaofu Jin Huamin Qu Yukang Yan Hong Kong University of Science and Technology Hong Kong China IIP(Computational Media and Arts) The Hong Kong University of Science and Technology Hong Kong SAR Hong Kong China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Computer Science University of Rochester Rochester New York USA

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Intelligent IoT Driven Ecosystem for Real-Time Air and Noise Pollution Detection

Intelligent IoT Driven Ecosystem for Real-Time Air and Noise...

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Cognitive Computing in engineering, Communications, sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Shiv Narain Gupta Mohd Firoz Warsi Nitin Kumar Chauhan Rakhi Bhardwaj Mohd Jawed Khan Ashish Choudhary Department of Electronics and Communication Engineering Greater Noida Institute of Technology (Engineering Institute) Greater Noida Uttar Pradesh India Department of Computer Science & Engineering(AIDS) Greater Noida Institute of Technology (Engineering Institute) Greater Noida Uttar Pradesh India Department of Electronics and Communication Engineering Indore Institute of Science and Technology Indore Madhya Pradesh India Department of Artificial Intelligence and Machine Learning College of Smart Computing CORE University Roorkee Haridwar Uttarakhand India Department Computer Science & Engineering (AI&ML) Greater Noida Institute of Technology (Engineering Institute) Greater Noida Uttar Pradesh India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

The high speed of urbanisation and industrialisation necessitate keeping track of environmental parameters like air quality and noise pollution for the sustainability of ecology as well as the health of the environment and public health. By facilitating real-time data collection, analysis, and management, the Internet of Things (IoT) addresses challenges in an efficient way. The present work introduces an IoT based smart system for the real-time detection of both air and noise pollution. The developed system employs strategically placed sensor nodes to gather data on air quality parameters (e.g., particulate matter (PM2. 5, PM10), carbon monoxide (CO), nitrogen dioxide (NO 2), sulfur dioxide (SO 2), and ozone (O 3), along with sound levels measured in décibels (dB). These nodes have inexpensive sensors and wireless communication modules, sending the data to a central server for data processing.

关键词： Wireless communication Pollution Noise Air quality Real-time systems Internet of Things Sulfur Sustainable development Monitoring Wireless fidelity

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U-SwinTrans: Automated Skin Lesion Segmentation Using Swin Transformer

U-SwinTrans: Automated Skin Lesion Segmentation Using Swin T...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Aaditya Lochan Sharma Kalpana Sharma Utkarsh Prakash Srivastava Palash Ghosal Department of Computer Science and Engineering Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India Dept of Electrical and Computer Engineering New York University New York USA Department of Information Technology Sikkim Manipal Institute of Technology Sikkim Manipal University Sikkim India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Skin lesions are difficult to segment because of their different shapes, sizes, colors, and textures. While Convolutional Neural Networks (CNNs) have proven effective at medical image segmentation, typical U-shaped networks that employ convolutional operations may struggle to capture global features successfully. To overcome these challenges, we incorporated Swin Transformer Blocks into a U-shaped architecture, enhancing the network’s capability to capture global features more effectively. We also used traditional image enhancement techniques such as DCT, DWT, and FFT to preprocess input images for better model performance. Our method entailed tokenizing preprocessed pictures into patches and putting them into the U-SwinTransformer encoder-decoder architecture, which learned both local and global semantic characteristics required for successful lesion segmentation. A comprehensive analysis of the ISIC 2017 dataset proves the effectiveness of our approach in skin lesion segmentation.

关键词： Image segmentation Shape Computational modeling Semantics computer architecture Transformers Skin Lesions Intelligent systems Testing

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Smooth Imaging System Using Learning Generative Adversarial Network 5th

Smooth Imaging System Using Learning Generative Adversarial ...

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5th International Conference on Recent Developments in Control, Automation and Power engineering, RDCAPE 2023

作者： Saraswat, Shipra Rani, Kajal Kanotra, Ishita Singh, Aditya Kumar Amity School of Engineering and Technology Amity University Noida Noida India Department of Computer Science and Engineering ABES Institute of Technology Affiliated to AKTU Uttar Pradesh Lucknow Ghaziabad India

ISBN: (纸本)9789819600465

A method for smoothing the blur images by deblurring them using GAN is discussed in the paper. The method includes learning techniques to detect the type of filters applied to the image, all possible edges present in the image and counting all those details into account. This method makes assumptions about the filters on the image and uses techniques to de-filter all the affected pixels. This method can also handle the unconstrained blurs caused due to overexposure, underexposure, shaking of the camera lens, zooming of pixels and optical noise. Due to these unconstrained measures of the photograph, the image becomes blurred or semi-blur. The proposed method also focuses on constrains of prior information about blurring filters and parametric definitions of filters used. This method detects all possible blurred edges in its own way using the contrast of all blurry areas. The colour contrasts of blurred images are different from that of deblurred images. Also, it can work on both single-frame and multi-frame scenarios. The constrained parameters of the blurry areas in multi-frames are used in achieving improved quality images. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Discriminators

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Bees Swarm Optimization Based Prediction of Machined Fatigue Failure

Bees Swarm Optimization Based Prediction of Machined Fatigue...

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Automation and Computation (AUTOCOM), International Conference on

作者： Sumithra M S. Sahebzathi Nikhat Parveen Arun K. K Ganesh Madhukar Dahane U Nagabalan Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of CSE Velammal College of Engineering and Technology Madurai India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh India Department of Mechanical Engineering Kumaraguru College of Technology Coimbatore Tamilnadu India Dr. Vithalrao Vikhe Patil College of Engineering Ahmedhnagar Maharashtra India Department of Science and Humanities – General Engineering R.M.D. Engineering College Chennai India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Artificial intelligence approach is now able to anticipate fatigue failure of machined components based on elementary key numbers without the need for professional knowledge of evaluation processes. In this paper, we develop a meta-heuristic approach for the prediction of defects using Bees Swarm Optimisation (BSO) in machined components. The study mainly predicts the fatigue failure by training the BSO experimentally with the training datasets. The three most essential input variables are the load stress, the hardness of the material, and the size of the fatal flaw (KD). In the context of fatigue testing, the only outcomes that may be determined are failure or non-failure. In this investigation, a single data set is generated for each specimen that is examined. When a full fracture has occurred at a given stress level, fracture surface analysis estimates the size of the deadly flaw that has formed. The architecture and hyperparameters of the neural network are cross-validated using a K-fold technique in order to get the highest possible prediction accuracy. After all is said and done, a cautious mean fatigue failure prediction accuracy of 92% is achieved.

关键词： Training Accuracy Machine learning Fatigue Production facilities Wind turbines Surface cracks Particle swarm optimization Stress Testing

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Multimodal Medical Image Registration Using Constraint Least Squares 15th

Multimodal Medical Image Registration Using Constraint Least...

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15th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2023 and 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2023

作者： Nagarathna, P. Jeelani, Azra Tirumala Vasu, G. Fiza, Samreen Kubra, Afreen Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Karnataka Bengaluru560064 India Department of Electronics and Communication Engineering Presidency University Karnataka Bangalore560064 India

ISBN: (纸本)9783031810794

Multimodal image registration finds widespread use in medical image analysis because of its usefulness in bringing together seemingly disparate sets of information from several imaging modalities. The major issue associated with image registration is that an algorithm works efficiently only for a given modality. In order to tackle this issue, we have proposed a novel multimodal image registration algorithm using Constrained Least Squares (CLS) that works for multiple medical modalities. The entire procedure of registration is based on the stipulation that the source and template images do not differ in zoom level but do differ in rotation and translation effects in the complex domain. To reduce the impact of noise and other inhomogeneities, a weight matrix is developed using prior knowledge of the local properties in the images. The least squares method is used to select the closed form solution to register the transformed image by estimating the displacement and rotation parameters. Many medical image registration algorithms have the limitation of quantitative analysis which we have addressed through Information Ratio (IR), Mutual Information Ratio (MIR), Lower bound Information Ratio (LIR), and Lower bound Mutual Information Ratio (LMIR). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image correlation

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