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A Novel Enhanced Approach for Security and Privacy Preserving in IoT Devices with Federal Learning Technique

SN computer science 2024年第6期5卷 750页

作者： Moeed, Syed Abdul Karnati, Ramesh Ashmitha, G. Mohammad, Gouse Baig Mohanty, Sachi Nandan Department of Computer Science and Engineering Kakatiya Institute of Technology and Science Department of Computer Science and Engineering Vardhaman College of Engineering School of Computer Science & amp Engineering (SCOPE) VIT-AP University

The Internet of Things (IoT) has revolutionized our lives, but it has also introduced significant security and privacy challenges. The vast amount of data collected by these devices, often containing sensitive information, makes them prime targets for cyberattacks. Traditional security methods struggle to keep pace with the evolving threat landscape of the interconnected IoT world. In response, this paper explores the transformative potential of federated learning (FL) in safeguarding both the privacy and security of IoT devices. FL keeps data on individual devices, only sharing updated models, not raw data, thus protecting user privacy and eliminating the need for a central data storage server, which reduces the risk of data breaches and security vulnerabilities. The effectiveness of the proposed model is evaluated using established open-source datasets like NSL-KDD and UNSW NB15, ensuring real-world applicability. Analysis of the dataset's features enabled the development of a model utilizing federated learning. Notably, the proposed model achieved superior performance with FL in detecting attacks on IoT networks. Furthermore, this research investigates the transformative potential of FL to address the inherent security and privacy challenges plaguing traditional, centralized data collection methods in the ever-expanding realm of IoT devices. FL empowers collaborative learning on distributed datasets, allowing devices to collectively improve security without compromising user privacy. The findings of this study demonstrate the promise of FL as a secure and privacy-preserving solution for the future of IoT. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Cyberattacks Federal learning Internet of Things Machine learning Security and privacy

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Secure data authentication and access control protocol for industrial healthcare system

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Journal of Ambient Intelligence and Humanized Computing 2023年第5期14卷 4853-4864页

作者： Gupta, Daya Sagar Mazumdar, Nabajyoti Nag, Amitava Singh, Jyoti Prakash Department of Computer Science and Engineering Rajiv Gandhi Institute of Petroleum Technology Jais UP Amethi229304 India Department of Information Technology Indian Institute of Information Technology Allahabad Prayagraj India Department of Computer Science and Engineering Central Institute of Technology Kokrajhar BTR Kokrajhar India Department of Computer Science and Engineering National Institute of Technology Patna Patna India

Because of recent COVID-19 epidemic, the Internet-of-Medical-Things (IoMT) has acquired a significant impetus to diagnose patients remotely, regulate medical equipment, and track quarantined patients via smart electronic devices installed at the patient’s end. Nevertheless, the IoMT confronts various security and privacy issues, such as entity authentication, confidentiality, and integrity of health-related data, among others, rendering this technology vulnerable to different attacks. To address these concerns, a number of security procedures based on traditional cryptographic approaches, such as discrete logarithm and integer factorization problems, have been developed. All of these protocols, however, are vulnerable to quantum attacks. This paper, in this context, presents a data authentication and access control protocol for IoMT systems that can withstand quantum attacks. A comprehensive formal security assessment demonstrates that the proposed algorithm can endure both current and future threats. In terms of data computing, transmission, and key storage overheads, it also surpasses other related techniques. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Authentication

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Heart Disease Risk Analysis and Assessment: A Data Driven Approach

Heart Disease Risk Analysis and Assessment: A Data Driven Ap...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： Kumar, B.H. Manjunatha Kumar, Abhinav Iyer, Aditya Soumika, M.L. Mayuri, S. SJC Institute of Technology Computer Science & Engineering Chikkaballapur India SJC Institute of Technology Department of Computer Science and Engineering Chikkaballapur India

ISBN: (纸本)9798350359688

The heart is indispensable for body. Diagnosis of heart disease must be complete and accurate. Heart disease is a dangerous disease. This disease is caused by many problems such as excessive stress, diabetes, high blood pressure, cholesterol and other factors in the human body. This article reviews and predicts heart disease using Python and machine learning. We can anticipate the occurrence of this disease by analyzing various features within the dataset to analyze the patient's performance, we have compiled a dataset containing 13 scores and 38 individual values. The principal focus of this article is to enhance the exactness of cardiac disorder assesment through employing machine learning algorithms. © 2024 IEEE.

关键词： Support vector machines

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Fuzzy-GEC an Energy-Aware Hybrid Task Scheduling on the Cloud 2nd

Fuzzy-GEC an Energy-Aware Hybrid Task Scheduling on the Clou...

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2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Lalitha Devi, K. Deepa Thilak, K. Shanmuganathan, C. Kalaiselvi, K. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Chennai India

ISBN: (纸本)9789819995172

In an infrastructure cloud environment, task scheduling should focus on optimizing execution time and saving energy. The data center consumes a large amount of energy during the execution of the task. Energy-saving techniques reduce the amount of energy consumed based on proper task scheduling approaches. The existing fuzzy-based hybrid genetic algorithm considers job length to optimize the makespan. However this fuzzy genetic encoded chromosome (fuzzy-GEC) algorithm considers both makespan and energy consumption to optimize the calculation of fitness value for assigning the task to the virtual machine (VM) by considering the characteristics of the task and VM. According to these characteristics, a fuzzy genetic rule-based encoding scheme is developed to schedule the tasks onto the virtual machines to minimize makespan and energy consumption. The effectiveness of the new technique is evaluated against FCFS and conventional genetic scheduling algorithm using Google Cloud trace workload. The results show the efficiency of the developed approach for makespan and energy consumption. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Virtual machine

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A Review of Intelligent Configuration and Its Security for Complex Networks

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Chinese Journal of Electronics 2024年第4期33卷 920-947页

作者： Yue ZHAO Bin YANG Fei TENG Xianhua NIU Ning HU Bo TIAN Science and Technology on Communication Security Laboratory School of Computer and Information Engineering Chuzhou University School of Computing and Artificial Intelligence Southwest Jiaotong University School of Computer and Software Engineering Xihua University Cyberspace Institute of Advanced Technology Guangzhou University

Complex networks are becoming more complex because of the use of many components with diverse technologies. In fact, manual configuration that makes each component interoperable has breed latent danger to system security. There is still no comprehensive review of these studies and prospects for further research. According to the complexity of component configuration and difficulty of security assurance in typical complex networks, this paper systematically reviews the abstract models and formal analysis methods required for intelligent configuration of complex networks, specifically analyzes, and compares the current key technologies such as configuration semantic awareness, automatic generation of security configuration, dynamic deployment, and verification evaluation. These technologies can effectively improve the security of complex networks intelligent configuration and reduce the complexity of operation and maintenance. This paper also summarizes the mainstream construction methods of complex networks configuration and its security test environment and detection index system, which lays a theoretical foundation for the formation of the comprehensive effectiveness verification capability of configuration security. The whole lifecycle management system of configuration security process proposed in this paper provides an important technical reference for reducing the complexity of network operation and maintenance and improving network security.

关键词： Complex networks Intelligent configuration Configuration security Software defined network Lifecycle management

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Elevating IDS Capabilities: The Convergence of SVM, Deep Learning, and RFECV in Network Security 2

Elevating IDS Capabilities: The Convergence of SVM, Deep Lea...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Kumar, G Aditya Katiyar, Aditi Srinivasan, Kathiravan Computer Science Engineering Vellore Institute of Technology Vellore India School of Computer Science and Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798350328202

By presenting an improved Intrusion Detection System (IDS) that combines deep learning with support vector machines (SVM), this research increases network security. The main goal is to increase the accuracy of SVM detection by using a methodical feature selection and optimization technique that is tailored to the complexity of network intrusions. 35 out of the 42 features were chosen for feature selection using RFECV, an algorithmic feature selection technique in machine learning. To ensure that the features preserved are those that contribute most to the model's predictive capacity and that the redundant features are deleted, techniques such as RFECV and feature priority ranking using ExtraTreesClassifier take the model's performance into account during the feature selection process. To improve classifier performance, optimization strategies such as hyperparameter tuning are used, focusing on important data and cutting down on redundancies. The performance of several SVM kernel functions, including linear, polynomial, RBF, and sigmoid, is compared in the study. The Linear model combined with the deep learning model was shown to perform the best. Our model outperforms current IDS frameworks, as demonstrated by comparative analysis, confirming the efficacy of integrating SVMs with deep learning for real-time threat detection. The KDD Cup 99 dataset, which has been widely used as a benchmark for assessing the performance of different IDS models, was used for the work. It offers a consistent, varied, and large dataset so that researchers may evaluate and contrast their methods. Researchers can experiment with feature selection and reduction strategies to enhance IDS performance because of the dataset's broad set of features. © 2024 IEEE.

关键词： Support vector machines

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A Coherent Analytical Model to Strengthen Vehicular Communication in VANET Using Droptail Priority Queue

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Journal of The Institution of Engineers (India): Series B 2025年 1-12页

作者： Kumar, Satyam Sinha, Bam Bahadur Khare, Priyank Electronics and Communication Engineering Indian Institute of Information Technology Ranchi Jharkhand Ranchi834004 India Computer Science and Engineering National Institute of Technology Sikkim Sikkim Ravangla737139 India

People’s demand for vehicles has been increasing day by day over the last few decades. A survey tells us that over 50,000 vehicles run on the roads per day. Such a large number of vehicles causes traffic. A survey tells us that over 1200 road crashes occur everyday in India out of which 25% result in deaths. Most of these accidents occur due to a lack of communication between the vehicles. This has led to the attraction of researchers towards the Vehicular Ad Hoc Network (VANET) ensuring road safety by establishing an Intelligent Transportation System (Malhi in Comput Secur 89:101664, 2020). In VANET, due to the frequent movement of vehicles and rapid variations in topology, the communication link between vehicles may break. This paper analyzes these problems and comes up with a novel approach to overcome these and maintain the performance of the protocol. In order to assess the performance metrics for the proposed protocol, an analytical model has been created. Simulations have been carried out to validate it and have been compared against similar state of comparative methods. © The Institution of Engineers (India) 2025.

关键词： Vehicular ad hoc networks

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Optimizing CNN Performance for Heart Attack Detection through Grey Wolf Algorithm

Optimizing CNN Performance for Heart Attack Detection throug...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Aditya Kumar, G. Katiyar, Aditi Kannan, A. Vellore Institute of Technology Computer Science Engineering Vellore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India

ISBN: (纸本)9798350361537

Better patient outcomes and prompt care depend on early detection of heart attacks. In this current work, we use the infamous MIT-BIH Arrhythmia Dataset, a reference resource for cardiac abnormality recognition, to train and validate our models. We propose an improved method for heart attack diagnosis and detection which includes a CNN model that is optimized with the help of the Grey Wolf Optimization (GWO) technique. This novel integration aims to enhance feature extraction and classification processes by tackling the drawbacks of traditional deep learning methods. A comprehensive testing process has been conducted on the proposed CNN-GWO model versus well-known deep learning models, such as Artificial Neural Networks also known as ANN, as well as many state-of-the-art machine learning models used for heart attack detection such as SVM, Decision Tree, and other ensemble learning models such as XG Boost. Our findings demonstrate a significant increase in detection accuracy and efficiency when comparing the CNN-GWO model to its rivals. This work not only demonstrates the effectiveness of combining CNN with GWO, but also demonstrates how well the proposed model performs in detecting heart attacks. This work contributes to the advancement of medical diagnostic technology by offering a more reliable and accurate diagnostic instrument. It also opens new options for algorithmic advancements in the future that forecast heart attacks. © 2024 IEEE.

关键词： Diseases

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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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Advanced skin lesion detection via efficientNetB0 and vision transformer model with spatial-aware attention

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Multimedia Tools and Applications 2025年 1-19页

作者： Shaheen, Hera Singh, Maheshwari Prasad Singh, Amit Kumar Department of Computer Science and Engineering National Institute of Technology Patna Ashok Rajpath Bihar Patna800005 India

Due to the high incidence and possibly fatal nature of skin cancer, early identification is crucial for enhancing patient results. This paper presents a unique deep learning network, EfficientNetB0 ViT, to accurately classify skin lesions. The proposed method encompasses the scalability and efficiency of EfficientNetB0 with the global pattern recognition capabilities of Vision Transformers (ViT), strengthened by the Spatial-Aware Squeeze and Excitation (SASE) attention mechanism. This innovative technique improves classification performance by enabling the model to dynamically highlight the image’s most pertinent elements. SASE attention successfully recalibrates feature importance, reducing overfitting and improving generalization. This research highlights the effectiveness of SASE attention mechanism enhances skin lesion classification, providing a valuable tool for dermatologists in early diagnosis. The model outperformed numerous methods, achieving a high accuracy of 94.64% as well as average precision of 95%, average recall of 95%, and F1-scores of 95% on HAM10000 dataset. It achieves an accuracy of 93% © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Diseases

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