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A Comprehensive Integrated Security Model with Advanced Access Control for Enhancing WSN computing in IoT Frameworks

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Tehnicki Vjesnik 2025年第3期32卷 1040-1046页

作者： Navaneethan, S. Arun, A. Department of Networking & Communications School of Computing SRM Institute of Science and Technology SRM Nagar Kattan Kulathur Tamilnadu 603203 India Department of Networking & Communications School of Computing Faculty of Engineering & Technology SRM Institute of Science and Technology Kattan Kulathur Tamilnadu 603203 India

Wireless Sensor Network (WSN) computing has emerged as a pivotal intermediary process addressing latency challenges inherent in Cloud-oriented Internet of Things (IoT) services. However, this advancement brings forth significant security and privacy concerns exacerbated by the proliferation of network nodes and the vast volumes of real-time data generated within IoT frameworks. To mitigate these issues, this research proposes an Integrated Security Model with Advanced Access Control (ISM-AAC) Mechanism. The ISM-AAC integrates Functional Encryption (FE) and Role-Based Access Control (RBAC) to fortify communication security. The model employs a comprehensive approach encompassing Access Control Processes, Integrated Encryption Processes, and Re-Encryption Processes to enhance data confidentiality and integrity. The detailed framework includes phases such as system initialization, user management, and access permission administration, ensuring robust security throughout data transactions. Evaluation of the model's efficacy is conducted based on various security metrics pertinent to WSN computing environments. This study contributes to the advancement of secure computing in IoT by proposing a sophisticated ISM-AAC framework that not only addresses current security challenges but also anticipates future threats in the landscape of WSN computing. The findings underscore the model's effectiveness in safeguarding sensitive IoT data and lay the groundwork for future enhancements in IoT security protocols. © 2025, Strojarski Facultet. All rights reserved.

关键词： Access control models

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CSG_IACO: an efficient stigmergic-based improved ACO routing strategy to determine the effective path based on traffic density in V2V networks

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1883-1908页

作者： Kumari, J. Jesy Janet Thangam, S. Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India

Vehicular ad hoc networks (VANETs) are an essential element and building block of the autonomous vehicle system. VANETs, a subcategory of mobile ad hoc networks (MANETs), stand out due to certain predetermined attributes. In VANETs, optimal path routing offers solutions to address issues like traffic density, node mobility, link failure, and bandwidth. Finding the optimal VANET road path between source and destination conditions is difficult. Extensive paths increase network overhead, communication costs, and failure probability, reducing routing efficiency. We present the new traffic density-based stigmergic ant colony optimization routing (CSG_IACO) to improve routing performance. Improved ant colonies stimulate the proposed CSG_IACO, which measures inter-vehicular density. Ant behavior serves as the inspiration for the improved ACO technique, a meta-heuristic methodology. Phase 1 of the CSG_IACO involves using the traffic density to form a cluster among the vehicles. Phase 2 of the suggested approach is Stigmergy-based ant colony optimization, which guides ants to indirectly communicate with each other to act upon the change in the environment to find a best path. This improves protocol efficiency in all aspects. The simulations using NS 2.35 and SUMO indicate that the proposed system considerably reduces obstacles to finding the optimal path compared to existing systems. The simulations show that our suggested scheme performs better than AODV-DATRLD, ACO-DATRLD, and CaCOIoV in terms of performance including the count of delivered packets, throughput, end-to-end delay, the average count of cluster formations per cluster head, and the number of dropped packets. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Ant colony optimization Cluster Optimal path QoS Stigmergy Traffic density VANETs

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Adapted Runge-Kutta Methods for Nonlinear First-Order Delay BVPs with Time-variable Delay

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Acta Mathematicae Applicatae Sinica 2025年第2期41卷 400-413页

作者： Cheng-jian ZHANG Yang WANG Hao HAN School of Mathematics and Statistics Huazhong University of Science and TechnologyWuhan 430074China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and TechnologyWuhan 430074China

This paper deals with numerical solutions for nonlinear first-order boundary value problems(BVPs) with time-variable delay. For solving this kind of delay BVPs, by combining Runge-Kutta methods with Lagrange interpolation, a class of adapted Runge-Kutta(ARK) methods are developed. Under the suitable conditions, it is proved that ARK methods are convergent of order min{p, μ+ν +1}, where p is the consistency order of ARK methods and μ, ν are two given parameters in Lagrange interpolation. Moreover, a global stability criterion is derived for ARK methods. With some numerical experiments, the computational accuracy and global stability of ARK methods are further testified.

关键词： delay boundary value problems adapted Runge-Kutta method Lagrange interpolation error analysis global stability

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Crypto Trading Platform: MERN Stack with Real-Time Data and Social Features 2

Crypto Trading Platform: MERN Stack with Real-Time Data and ...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Gurupriya, M. Mouhitha, A. Gujjula, Saranya Ankireddypalli, Okesh Reddy Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bengaluru India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science & Engineering Bangalore India

ISBN: (纸本)9798331505745

The increasing complexity of cryptocurrency markets necessitates the development of efficient portfolio management tools that provide real-time tracking, price updates, and market awareness. This paper focuses on an advanced concept of a cryptocurrency wallet service provision that provides a summary of available coins, their prices, available quantities, and customers' transaction histories. Developed for the 'frontend' using *** and for the 'backend' using ***, which uses the CoinGecko API for receiving live market prices and collecting curated news blog articles from other APIs. The data related to the wallets are efficiently managed since the application is built based on the MongoDB schema, and in addition to this, the user authentication is also integrated. A responsive design of the user interface allows for showing both a portfolio summary and the latest news related to cryptocurrencies, thus suggesting an 'everything in one place' solution for users interested in cryptocurrencies. This system shows how real-time data integration could be implemented, how the system is friendly to users, and how the system manages the content. This system could be expanded in the future with predictive analytics and an AI recommendation system for the portfolio management and contents. © 2025 IEEE.

关键词： Financial markets

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DeepLeuk: a convolutional neural network pre-trained model for microscopic cell images-Based leukemia Cancer analysis

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Multimedia Tools and Applications 2025年第14期84卷 13809-13842页

作者： Yenurkar, Ganesh Keshaorao Mal, Sandip Thakur, Nileshsingh Dhomne, Shrawani Dhurve, Merula Patel, Mayank Kulmeti, Karan Dhurve, Harsh Yeshwantrao Chavan College of Engineering Wanadongri Nagpur India School of Computing Science & Engineering VIT Bhopal University Bhopal India VIT Bhopal University Bhopal India

The blood and bone marrow are affected by leukemia, a deadly kind of cancer, that significantly impacts the quality of life of those diagnosed. Early identification and precise diagnosis are crucial for improving survival rates. Fortunately, recent advancements in medical image analysis, particularly deep learning-based techniques, have greatly improved the ability to distinguish leukemia cells from healthy ones through microscopic cell images. This research introduces a deep learning-based leukemia cancer classifier, specifically a CNN pre-trained model, utilizing microscopic cell images to detect malignant cells. Using pre-processing techniques such as picture scaling, Region of Interest (RoI) extraction, and Improved Anisotropic Filtering (IAF) and feature extraction, the blood cell image dataset is first cleaned. After that leukemia-affected and healthy cells are evaluated using various classification algorithms and neural networks, with optimal features identified to improve classifier performance. The results suggest that neural networks function well as a classifier algorithm to detect whether the person is cancerous or non-cancerous, with the proposed CNN pre-trained model providing precision of 98.9%, which is higher than any other method mentioned. The proposed model prioritizes recall, a key performance metric, to reduce the number of false negatives. Accurate diagnosis and treatment are critical, as misdiagnosing a patient with cancer as not having cancer can lead to severe consequences. With the main objective of minimizing inadvertent mistakes made by physicians, the proposed model performs better than kNN, Decision Trees, Random Forest, SVM, and Logistic Regression models. Using deep learning-based techniques to improve cancer diagnosis and treatment is essential. Improving survival rates and the quality of life for individuals with leukemia requires early identification and accurate diagnosis. This research can help doctors make more accurate diagnos

关键词： Image classification

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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Optimizing DDoS Attack Detection Using Machine Learning 2

Optimizing DDoS Attack Detection Using Machine Learning

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Aggarwal, Saransh Behera, Bhagrajyoti Singh, Murari Kumar Sharma, Ajeet Kumar School of Computing Science and Engineering Sharda University Greater Noida India School of Computer Science and Engineering Sharda University Greater Noida India

ISBN: (纸本)9798331530389

The increase in the Distributed Denial of Service attack (DDoS) leads to a significant threat to the network security. Inability to timely and accurately detect DDoS attacks disrupts services offered by companies and the government causing financial losses. Additionally, prolonged DDoS attacks can spoil user experience and degrade company reputation. The recent literature reveals various techniques for detecting DDoS attacks, including pre-trained machine learning and deep learning algorithms. This research paper addresses this challenge by tweaking random forest algorithms for maximum accuracy using CICDDoS2019 dataset. The methodology includes collecting of dataset, data processing, feature scaling and training multiple machine learning algorithms to identify maximum accuracy for detecting DDoS attacks. The result shows improvement of accuracy and precision of machine learning models used. Future work may include training the model for other types of attacks, real time implementation of the model developed, developing a hybrid approach for detection and protection from DDoS attack. © 2025 IEEE.

关键词： Network security

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Identification and Classification of Medicinal Plants using Machine Learning Algorithms 3

Identification and Classification of Medicinal Plants using ...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Bhargavi, G. Jayaraman, Venkatesh Jabez, J. Deepa, R. Surendran, R. Srm Institute of Science and Technology Department of Computing Technologies Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Chennai600069 India Sathyabama Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology School of Computing Department of Computing Technologies Kattankulathur Tamilnadu Chennai603203 India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331509675

The increaing significance of plant life and botanical expertise extends beyond mere visual appreciation. With the growing interest in sustainable living and alternative remedies, there is a pressing demand for easily accessible and credible information on plant categorization, particularly focusing on medicinal plants. This innovative project synergizes machine learning capabilities, botanical proficiency, and user-friendly technology to give users the insights required to harness the benefits of medicinal plants responsibly and sustainably. The platform facilitates precise categorization and a comprehensive understanding of the therapeutic attributes of plants. © 2025 IEEE.

关键词： Medicinal chemistry

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Artificial intelligence algorithms for object detection and recognition in video and images

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Multimedia Tools and Applications 2025年 1-18页

作者： Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Department of Data Science and Business System School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India Department of Computer Science Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Birla Institute of Technology Mesra Ranchi India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Department of Computer Science Engineering Saveetha School Of EngineeringSaveetha Institute of Medical and Technical Sciences Chennai6021055 India Department of Netwoking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India

The usage of machine learning and deep learning algorithms have necessitated Artificial Intelligence'. AI is aimed at automating things by limiting human interference. It is widely used in IT, healthcare, finance, and agriculture. It is achieved through several deep learning algorithms that reflect the human brain's intelligence. These AI algorithms can be manipulated according to changing needs and improved efficiency. This paper tries to utilize the developments made in AI technology to classify the images and recognize the objects present in them. One widely used AI algorithm is CNN (Convolutional Neural Networks). The CNN is a deep learning-based algorithm that consists of various layers that extract and filters the parameters present in the images. Some additional layers of ResNet50 and the CNN algorithm are used to extract the parameters to improve image recognition accuracy. The image dataset taken for training and testing the proposed model is imageNet. The images are initially processed before sending them to the proposed model. The proposed model is trained, validated, and tested through the images obtained after the initial processing. The same process is repeated several times until getting the maximum accuracy. The accuracy of the proposed model in terms of image recognition is recorded. The obtained results are compared with other image classification algorithms like VGG16 and VGG19. It is concluded that the proposed model outperforms other traditional methods in terms of accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Forward Secure Similarity Search Over Encrypted Data for Hamming Distance

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IEEE Transactions on Dependable and Secure computing 2025年第3期22卷 2478-2491页

作者： Wang, Beining He, Kun Huang, Yingying Chen, Jing Zhao, Chenbin Du, Ruiying Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China

Similarity search on encrypted data can identify similar data and handle misspelled keywords in a privacy-preserving manner and thus has received a lot of attention. However, existing schemes suffer from imprecise or predefined distance thresholds, which means that they do not always return the expected search results. Moreover, these schemes either do not consider document addition or lack forward security in this dynamic setting. In this article, we present a Similar Keyword Matching (SKM) framework that accurately calculates the Hamming distance between keywords through a new keyword representation called uni-pos-gram. Based on our framework, we propose a basic scheme for similarity search over encrypted data called SimSE that offers adjustable Hamming distance thresholds and an enhanced scheme called SimSE-F that provides forward security. Security analysis demonstrates that our schemes effectively safeguard the privacy of documents, indexes, and searches. Empirical experiments using real-world datasets demonstrate the efficiency and practical applicability of our schemes. © 2004-2012 IEEE.

关键词： Differential privacy

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