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Secure container Orchestration: A framework for detecting and mitigating Orchestrator - level vulnerabilities

Multimedia Tools and Applications 2025年第17期84卷 18351-18371页

作者： Mahavaishnavi, V. Saminathan, R. Prithviraj, R. Department of Artificial Intelligence and Data Science Panimalar Engineering College Tamil Nadu Chennai600123 India Department of Computer Science and Engineering Annamalai University Chidambaram India Department of Computer Science and Engineering Annamalai University Chidambaram India

This paper presents Secure Orchestration, a novel framework meticulously planned to uphold rigorous security measures over the profound security concerns that lie within the container orchestration platforms, especially in Kubernetes. The widespread adoption of containerized environments has raised concerns about the vulnerabilities at the level of the orchestrator. Secure Orchestration delivers a comprehensive approach by leveraging cutting-edge methods to detect and prevent the exposure of vulnerabilities from orchestrator misconfigurations, privilege escalation exploits that can thwart the orchestration security layer, and unauthorized access attempts targeting the orchestration system itself. In addition, it is further strengthened by deploying an Intrusion Detection and Prevention System (IDPS), which is responsible for actively monitoring the orchestration infrastructure. The IDPS utilizes sophisticated algorithms and mechanisms for detecting anomalies to continuously assess the orchestration environment for indications of intrusion, vulnerabilities, and anomalous activities. By using this proactive approach, the framework is able to rapidly identify potential threats and respond through countermeasures in a timely manner, thus greatly reducing the risks associated with orchestrator level vulnerabilities. Conducting a set of real-world experiments that closely simulate a variety of threat scenarios is imperative for empirically validating the efficiency of this framework. The obtained empirical results yield insightful revelations on the efficacy of the framework in accurately identifying and mitigating vulnerabilities, thereby ensuring the privacy and integrity of container orchestration systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Containers

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Adaptive Wavelet Convolution Stochastic Analysis Graph Neural Network for Remaining Useful Life Prediction

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IEEE Sensors Journal 2025年第11期25卷 19428-19441页

作者： Hu, Qidi Su, Binyi Kong, Jinzhen Liu, Weipeng Hebei University of Technology School of Artificial Intelligence and Data Science Tianjin300130 China Hebei University of Technology School of Mechanical Engineering Tianjin300130 China

The prediction of the Remaining Useful Life (RUL) of stochastically degrading devices is of critical importance for Prognostics and Health Management (PHM). However, most of the existing approaches overlook the inherent coupling of trends and fluctuations in the degradation process. Meanwhile, these methods are struggling to effectively model the random fluctuations component in the degradation process and may lead to inaccurate prediction. To address these limitations, we propose a modified RUL prediction framework WTSAGNN. The framework extends the spatiotemporal dependencies of multi-sensor signals into the frequency domain. By leveraging the proposed adaptive orthogonal wavelet convolution to more effectively decouple the trend and fluctuation components of the degradation process. Furthermore, we introduce a stochastic differential network (SDN) to replace the traditional multilayer perceptron (MLP). The proposed method enhances the robustness of neural networks against stochastic variations in degradation processes through explicit stochastic modeling. Experimental results demonstrate that WTSAGNN outperforms existing state-of-the-art(SOTA) methods on benchmark datasets, achieving up to a 9.1% improvement in prediction accuracy with only a limited increase in computational overhead. In addition, the proposed modules are plug-and-play, allowing seamless integration with existing data-driven prediction frameworks. © 2025 IEEE.

关键词： Graph neural networks

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A survey on cross-user federated recommendation

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science China(Information sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

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Improved Brain Tumour Prognosis Using Deep Neural Networks 4

Improved Brain Tumour Prognosis Using Deep Neural Networks

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4th IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Vasavi, D. Durgasathvika I, Vindhya VijayChandu, K. Shalini, K. Hariharan, Shanmugasundaram Krishnamoorthy, Murugaperumal Vardhaman College of Engineering Department of Artificial Intelligence and Data science Engineering Hyderabad India Vardhaman College of Engineering Department of Artificial Intelligence and Data Science Hyderabad India Vardhaman College of Engineering Department of Electrical and Electronics Engineering Hyderabad India

ISBN: (纸本)9798350352931

The segmentation of brain tumor in MRI is an essential step to specify the most likely location, stage and type of a lesion being diagnosed by physician before any medical treatment planning with full-confidence for better prognosis. Traditional manual annotation is expensive, labor-intensive and inconsistent. Deep-learning-oriented image analysis has recently brought new impetus to this quest, as for example in the form of convolutional neural network (CNN) based models which offer some advantages over traditional methods, but may have difficulty in capturing more complex spatial relations. This paper proposes a novel framework for brain tumor segmentation using Dynamic Graph Neural Networks (DGNNs) and Dense Micro-block Difference Feature (DMDF) extraction. Here, DGNNs build and perform operations on graphs which enables them to learn complex spatial as well as contextual features. The DMDF improves feature representation by amplifying regional/micro-block differences on which accurate tumor boundary definition crucially depends. The proposed model integrates DGNNs with a fully convolutional neural network (FCNN), processing multi-modal MRI scans to generate precise segmentation masks. The training pipeline includes robust data preprocessing, normalization, and augmentation techniques for improved generalization. Evaluation on the Brain MRI Images for Brain Tumor Detection dataset demonstrates the effectiveness of this approach, achieving high Dice similarity coefficients and accurate segmentation performance. This method significantly enhances the accuracy and efficiency of brain tumor diagnosis and treatment planning. © 2024 IEEE.

关键词： Convolutional neural networks

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Assistance for Visually Impaired individuals using artificial intelligence and Natural Language Processing

Assistance for Visually Impaired individuals using Artificia...

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2025 IEEE International Students' Conference on Electrical, Electronics and Computer science, SCEECS 2025

作者： Sathvig, Sri K. Bharathi.ln, Priyanka Santhosh.m Ramya, J. St. Joseph's College of Engineering Artificial Intelligence and Data Science Chennai India

ISBN: (纸本)9798331529833

People with Visual Impairments are vulnerable to various challenges in their everyday lives ranging from Access to Information, Travelling to places, Recognizing their surroundings, Isolated Lifestyle and much more. The Proposed solution aims to address these socially inclusive problems through the use of artificial intelligence, Natural Language Processing, Machine Learning and Computer Vision. The feature index involves using Object Detection models like Yolo v5 for Obstacle avoidance, Integration of Google Map API for Seamless Navigation and NLP based Optical Character Recognition (OCR) for Text Recognition. An average accuracy of 86 percentage was achieved across all models. This solution aims to provide Better Lifestyle, Increased accessibility and Reduced Dependability for the Community of Visually Impaired Individuals. © 2025 IEEE.

关键词： artificial intelligence Computer Vision Machine Learning Natural Language Processing

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Deep Learning Technique for Medicinal Plant Leaf Identification: Using Fine-Tuning of Transfer Learning Model 4

Deep Learning Technique for Medicinal Plant Leaf Identificat...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Kalaiselvi, P. Esther, C. Aburoobha, A. Nishanth, J. Gopika, S. Kabilan, M. Sri Sairam Engineering College Department of Artificial Intelligence and Data Science Chennai India

ISBN: (数字)9798331508845

ISBN: (纸本)9798331508845

Ayurvedic medicines are used to treat various acute and chronic conditions with minimal side effects. They have played a significant role in global health systems. Numerous health issues, including pneumonia, cancer, diabetes mellitus, heart disease, asthma, hypertension, and others, can be naturally treated using plants. Because of their diverse pharmacological characteristics, plants rich in bioactive phytomedicine components, such as alkaloids, flavonoids, and polyphenols, are widely used to treat disorders. India is renowned for its rich medicinal plants repository, with ayurvedic medicinal practices considered a 'living tradition.' Their identification is a manual and time-consuming process, often requiring extensive knowledge of Ayurvedic and botanical classification. Thus,aiming to automate the identification of medicinal plants, our system utilizes a ResNet-18 model to extract key features from plant images, achieving high accuracy in identifying plant species. This method simplifies identification and also broadens the reach of traditional medicine knowledge. © 2024 IEEE.

关键词： Medicinal chemistry

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Multimodal Content Generation: A Project for AI Creativity 2

Multimodal Content Generation: A Project for AI Creativity

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Geetha, M.P. Hariharan, S. Jeevanprasanth, T. Sanjay, L. Sri Eshwar College of Engineering Department of Artificial Intelligence and Data Science Coimbatore India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

21st-century interactions involve the consumption of content, whether in the form of text, audio, images, or any other medium. This paper presents a multimodal content generation (MCG) system that seeks to ease the content production process by providing integrated solutions for video, image, audio and text content production among others. Large language models (LLMs) have shown a great promise in enhancing the creativity process in various fields such as media and education where mediums of communication interweave. The system takes into account the current data, situation, user needs, and composes an appropriate and suitable content for them. At the same time, conventional AI models, such as transformers, are combined with multimodal models enhanced with optimization strategies such as transfer learning and fine-tuning. This strategy greatly augments the content generation potential and scalability of the system and thus, is of great importance in the design of systems for the content production process. The quantum- inspired elements of the hybrid model are the ones targeted towards making changes in decision and feature selection within the creative processes. The effectiveness of MCG in this research was compared to other content creation systems, and the results of the research revealed that MCG is more creative, efficient, and satisfying for the users than the existing systems;therefore, it has a great potential for changing the practices of producing content in a multimedia environment. © 2024 IEEE.

关键词： Distribution transformers

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Malayalam Fake News Detection Using Optimized Convolutional Neural Network (OPCNN) 11

Malayalam Fake News Detection Using Optimized Convolutional ...

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11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Mohan, Aiswarya Iqbal, Nafla Mashpher, Manaal Rajagiri School of Engineering and Technology Department of Artificial Intelligence and Data Science Kakkanad India

ISBN: (纸本)9798350379136

These days, a range of issues are being caused by fake news, including satirical pieces, fake news, and intentional government propaganda. Because of the blathering social media discourse, the idea of "fake news"has recently evolved. Wide-ranging consequences of fake news dissemination include the development of distorted opinions and the tampering of election outcomes to benefit specific politicians. Spammers also use captivating news headlines to entice readers to click on their advertisements. These days, it can be quite difficult to tell if the news we hear is accurate. There aren't many methods for assessing authenticity, and the ones that are accessible are all difficult and beyond the capabilities of the average person. A fact-checking platform that gives us that chance is desperately needed. Deep Learning (DL) approaches are suited for more accurately and efficiently detecting false news because of their recent accomplishments in difficult natural language processing tasks. The suggested approach is a multi-layered, optimal CNN model for detecting fake news. The OPCNN and Word embedding are optimized for feature extraction using the hyperopt optimization method in order to produce highly accurate results. In this study, we generated word embeddings using the FastText technique. The purpose of the model is to identify news stories published in Malayalam that are either false or misleading. The model is trained on 80% of a newly compiled Malayalam dataset consisting of news articles from various online sources having 11,686 news items with 5843 false and real news entries apiece and then evaluated on the remaining 20%. The evaluation's findings show that the suggested model works better than current methods and has a high degree of accuracy when it comes to identifying false information in Malayalam. © 2024 IEEE.

关键词： Convolutional neural networks

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Gesture Based Sign Language Recognition for Specially Challenged Using Yolov5

Gesture Based Sign Language Recognition for Specially Challe...

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2024 International Conference on science, Technology, engineering and Management, ICSTEM 2024

作者： Geetha, M.P. Swetha, S. Subitsha, M. Visnupriya, K.V. Department of Artificial Intelligence and Data Science Sri Eshwar College of Engineering Coimbatore India

ISBN: (纸本)9798350376913

Sign language is a vital mode of communication for individuals with hearing and speech impairments, enabling them to interact with the world around them. This paper presents a comprehensive study and implementation of a Gesture-Based Sign Language Recognition system designed to empower specially challenged individuals by providing them with an effective means of *** proposed system leverages computer vision,machine learning and deep learning techniques to recognize and interpret gestures, converting them into text or *** system has the potential to be integrated into various assistive devices, mobile applications, and educational platforms, making sign language communication more accessible and empowering individuals with hearing and speech impairments. © 2024 IEEE.

关键词： Computer vision

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Multi-Disease Prediction and Classifier: A Comprehensive Approach for Healthcare Decision Support 5

Multi-Disease Prediction and Classifier: A Comprehensive App...

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5th IEEE International Conference for Emerging Technology, INCET 2024

作者： Sudha, S.V. Priya, Kamali S. Krishnapriya, P. Prathiksha, P.N. Department of Artificial Intelligence and Data Science KPR Institute of Engineering and Technology Coimbatore India

ISBN: (纸本)9798350361155

The speedy identification and early detection of diseases that are deadly are greatly important when it comes to human life saving. On the other hand, the existence of poor medical system alongside the lack of equal distribution of healthcare presents a barrier towards early disease detection leaving death as the only option. One of the ways to tackle these problems is the utilization of machine learning in detection and analysis of diseases risk is machine learning promising. Utilizing predictive analytics in medicine, practitioners can make accurate and relevant medical insights promptly, relying on the information they have. The work focused on generating prediction models to identify seven most severe illnesses: diabetes, pneumonia, malaria, heart disease, kidney disease, breast cancer, and liver disease, using an algorithm of Random Forest Classifier. Alongside this, the Convolutional Neural Networks were utilized for the detection of malaria and pneumonia. Users can get better knowledge about their health problems and at the same time reduce the access constraints to healthcare by using machine learning. This research is also about designing a web app with the Flask framework and will give its consumers an easy-to-use tool for the simultaneous prediction of all seven diseases. Such a user interface grants better healthcare access and results in the use of proactive measures, as people can take a more active role in their wellness. © 2024 IEEE.

关键词： Diseases

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