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A comprehensive survey on 6G-security: physical connection and service layers

Discover Internet of Things 2025年第1期5卷 1-30页

作者： Saeed, Mamoon M. Saeed, Rashid A. Hasan, Mohammad Kamrul Ali, Elmustafa Sayed Mazha, Tehseen Shahzad, Tariq Khan, Sunawar Hamam, Habib Sana00967 Yemen Department of Computer Engineering College of Computers and Information Technology Taif University P.O. Box 11099 Taif21944 Saudi Arabia Department of Electronics Engineering College of Electronics Engineering Sudan University of Science and Technology Omdurman Sudan Bangi43600 Malaysia Department of Electrical and Electronic Engineering Faculty of Engineering Red Sea University Port Sudan33312 Sudan School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Engineering COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Computer Science and Software Engineering International Islamic University Islamabad44000 Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa BP 1989 Av. Grandes Ecoles Libreville Gabon Bridges for Academic Excellence-Spectrum Tunis Tunisia Department of Computer Science School Education Department Government of Punjab Layyah31200 Pakistan Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India

After adopting 5G technology, businesses and academia have started working on sixth-generation wireless networking (6G) technologies. Mobile communications options are expected to expand in areas where previous generations could not do so. 6G networks are anticipated to be constructed using various diverse technologies. These encompass diverse cutting-edge advancements, such as distributed ledger systems like blockchain, visible light communications (VLC), post-quantum cryptography, edge computing, molecular communication, THz, and other advances. These advances necessitate a reassessment of previous security strategies from a security perspective. In the future, networks must adhere to stricter criteria for authentication, encryption, access control, connectivity, and detection of harmful activities. Ensuring privacy and dependability necessitates the implementation of supplementary security protocols. The essay explores the primary concerns and challenges related to the security of the 6G network. This paper describes the improvements in security in communications from 1G through 6G. This paper divides security in the sixth generation into three layers: physical, connection, and service. Each layer-by-layer discusses the standard technologies and security issues for each technology proposed in each sixth-generation security layer. All proposed solutions for each of the three layers are discussed in Sixth Generation Security. It also reviews all proposed solutions for each layer, indicating the proposed solution and its limitations. © The Author(s) 2025.

关键词： 5G mobile communication systems

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AI-Driven Security Enhancements in Wireless Sensor Networks 2

AI-Driven Security Enhancements in Wireless Sensor Networks

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2nd IEEE International Conference on Contemporary computing and Communications, InC4 2024

作者： Singh, Kamred Udham Kumar, Ankit Varshney, Neeraj Raja, Linesh Singh, Teekam Dewangan, Amit Kumar School of Computing Graphic Era Hill University Dehradun India Guru Ghasidas Vishwavidyalaya Department of Information Technology Bilaspur India GLA University Department of Computer Engineering & Applications UP Mathura India Manipal University Jaipur Department of Computer Applications Jaipur India Graphic Era Deemed to be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350383652

Wireless sensor networks have garnered significant adoption as a technology for collecting and analyzing data from the environment. Their versatility and wide-ranging applications, encompassing critical military operations, forest fire detection, healthcare systems, and civilian uses, have made them a focal point of extensive research. The potential of WSNs to provide valuable insights and address various real-world challenges has spurred significant interest and exploration in the scientific community. Nevertheless, Wireless Sensor Networks (WSNs) are susceptible to security threats, especially in unattended environments. To address these threats, the popular Low-Energy Adaptive Clustering Hierarchy protocol is deployed with machine learning techniques. This paper makes an analysis of different machine learning algorithms with multiple class of parameters and evaluates it with criterion as such favorable for a WSN. The Random Forest technique, which allows for the classification of assaults such as blackhole attacks, grayhole attacks, TDMA attacks, and flooding attacks, is the most suitable algorithm for intrusion detection. It comprises six extracted characteristics. The accuracy score is 99.12% with prediction accuracy for attacks: 87%, 89%, 92% and 93% respectively. Unlike detecting the anomaly alone, this mechanism when given to a centralized management system become a smart governance model. This prevents the network from different intrusion methodologies and enhance the characteristics such as energy efficiency, network lifetime, throughput, performance and security issue of the wireless sensor network. © 2024 IEEE.

关键词： Random forests

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Dementia Detection Using Machine Learning in Males and Females

Dementia Detection Using Machine Learning in Males and Femal...

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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Iyer, Aishwarya Hariharan Vekkot, Susmitha Gupta, Deepa Gopalakrishnan, E.A. School of Computing National University of Singapore Singapore Amrita School of Engineering Amrita Vishwa Vidyapeetham Dept. of Electronics and Communication Engineering Bangalore India Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science and Engineering Bangalore India Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Bangalore India

ISBN: (纸本)9798350379716

In this study, we train different Machine Learning classifiers on male and female samples from The ADReSS Challenge dataset. After testing them on data consisting of both male and female samples as well as on data with samples of only the corresponding sex, we use SHapley Additive exPlanations on the models to identify the most important features. Then, we present the results of training the models with only the most important features identified. Finally, we present a comparative analysis based on the observations and insights gathered through these experiments. © 2024 IEEE.

关键词： Adversarial machine learning

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Blockchain Based Security Framework for Internet of Medical Things (IoMT) Applications 7

Blockchain Based Security Framework for Internet of Medical ...

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7th International Conference on Advances in Artificial Intelligence, ICAAI 2023

作者： Baniya, Pashupati Nand, Parma Bhushan, Bharat Hameed, Alaa Ali Jamil, Akhtar Department of Computer Science and Engineering School of Engineering & Technologynce and Enginerring Sharda University Greater Noida India Department of Computer Engineering Istinye University Istanbul Turkey Department of Computer Science National University of Computer and Engineering Sciences Islamabad Pakistan

ISBN: (纸本)9798400708985

The growing demand for Internet of Medical Things (IoMT) sensors within the healthcare sector has emphasized the necessity for robust security measures. However, these devices, often constrained by limited resources, frequently require external assistance to guarantee the security of data. Blockchain technology emerges as a practical solution, offering a way to enhance system security and safeguard patients' sensitive health records against unauthorized access and data breaches. The fusion of IoMT and blockchain presents a range of benefits, such as cost reduction, improved speed, automation, and the guarantee of unmodifiable data. Nevertheless, security concerns persist, particularly in light of the ever-evolving encryption techniques. The effective implementation of IoMT hinges on the delivery of personalized, patient-centered care, which has the potential to enhance affordability, simplicity, and overall convenience, ultimately contributing to improved quality of life and extended lifespans. In this comprehensive exploration, we delve into the historical context, architecture, components, and challenges associated with IoMT. Additionally, we conduct an in-depth examination of blockchain technology, covering its essential elements and categories, as well as its seamless integration into IoMT, underscoring its critical role in reinforcing security measures within the healthcare domain. Furthermore, this paper outlines recent developments in blockchain technology within the IoMT framework, highlighting its ongoing evolution and sustained significance in the healthcare field. © 2023 ACM.

关键词： Blockchain

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KSKV:Key-Strategy for Key-Value Data Collection with Local Differential Privacy

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computer Modeling in engineering & sciences 2024年第6期139卷 3063-3083页

作者： Dan Zhao Yang You Chuanwen Luo Ting Chen Yang Liu Artificial Intelligence Development Research Center Institute of Scientific and Technical Information of ChinaBeijing100038China Industry Development Department NSFOCUS Inc.BeijingChina School of Information Science and Technology Beijing Forestry UniversityBeijing100083China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu610054China Institute of Computing Technology China Academy of Railway Sciences Corporation LimitedBeijing10081China

In recent years,the research field of data collection under local differential privacy(LDP)has expanded its focus fromelementary data types to includemore complex structural data,such as set-value and graph ***,our comprehensive review of existing literature reveals that there needs to be more studies that engage with key-value data *** studies would simultaneously collect the frequencies of keys and the mean of values associated with each ***,the allocation of the privacy budget between the frequencies of keys and the means of values for each key does not yield an optimal utility *** the importance of obtaining accurate key frequencies and mean estimations for key-value data collection,this paper presents a novel framework:the Key-Strategy Framework forKey-ValueDataCollection under ***,theKey-StrategyUnary Encoding(KS-UE)strategy is proposed within non-interactive frameworks for the purpose of privacy budget allocation to achieve precise key frequencies;subsequently,the Key-Strategy Generalized Randomized Response(KS-GRR)strategy is introduced for interactive frameworks to enhance the efficiency of collecting frequent keys through group-anditeration *** strategies are adapted for scenarios in which users possess either a single or multiple key-value ***,we demonstrate that the variance of KS-UE is lower than that of existing *** claims are substantiated through extensive experimental evaluation on real-world datasets,confirming the effectiveness and efficiency of the KS-UE and KS-GRR strategies.

关键词： Key-value local differential privacy frequency estimation mean estimation data perturbation

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An Intrusion Detection System for MANET to Detect Gray Hole Attack using Fuzzy Logic System

An Intrusion Detection System for MANET to Detect Gray Hole ...

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2023 International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2023

作者： Bala, K. Paramesh, J. Elma, K. Johny Santhanalakshmi, S.T. Bharath Institute of Higher Education and Research School of Computing Department of Computer Science and Engineering Tamilnadu Chennai600073 India Easwari Engineering College Department of Information Technology Tamil Nadu Chennai600089 India Panimalar Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai600123 India

ISBN: (纸本)9781665474511

The Mobile Ad hoc network (MANET) is a kind of wireless communication network that is composed of several nodes which are highly movable throughout the various directions in the network. Due to nonexistence of infrastructure in the MANET, the nodes can interconnect with one another in a random fashion. The Intrusion Detection System (IDS) is an observing parameter which is used to identify the suspicious functioning in the network and intimates through alert to the security operation center. Based upon the constraints, the center examines the problem and solves the constraints using necessary steps and solution. The transformation of the gray hole attack. The important consequences in the wireless sensor network involves the grey hole attack. It is a revolution of the black hole attack. This leads to decrease the efficiency of the network. Thus, to overcome the various malicious attacks, the proposed system is implemented with fuzzy logic system to enhance the efficiency in the networking system. © 2023 IEEE.

关键词： Mobile ad hoc networks

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A full privacy-preserving distributed batch-based certificate-less aggregate signature authentication scheme for healthcare wearable wireless medical sensor networks (HWMSNs)

A full privacy-preserving distributed batch-based certificat...

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作者： Rabie, Osama Bassam J. Selvarajan, Shitharth Hasanin, Tawfiq Mohammed, Gouse Baig Alshareef, Abddulrhman M. Uddin, Mueen Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia Cybersecurity Center King Abdulaziz University Jeddah Saudi Arabia Department of Computer Science Kebri Dehar University Kebri Dehar Ethiopia School of Built Environment Engineering and Computing Leeds Beckett University LeedsLS1 3HE United Kingdom Department of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India College of Computing and Information Technology University of Doha for Science and Technology Doha24449 Qatar

The dynamic connectivity and functionality of sensors has revolutionized remote monitoring applications thanks to the combination of IoT and wireless sensor networks (WSNs). Wearable wireless medical sensor nodes allow continuous monitoring by amassing physiological data, which is very useful in healthcare applications. These text data are then sent to doctors via IoT devices so they can make an accurate diagnosis as soon as possible. However, the transmission of medical text data is extremely vulnerable to security and privacy assaults due to the open nature of the underlying communication medium. Therefore, a certificate-less aggregation-based signature system has been proposed as a solution to the issue by using elliptic curve public key cryptography (ECC) which allows for a highly effective technique. The cost of computing has been reduced by 93% due to the incorporation of aggregation technology. The communication cost is 400 bits which is a significant reduction when compared with its counterparts. The results of the security analysis show that the scheme is robust against forging, tampering, and man-in-the-middle attacks. The primary innovation is that the time required for signature verification can be reduced by using point addition and aggregation. In addition, it does away with the reliance on a centralized medical server in order to do verification. By taking a distributed approach, it is able to fully preserve user privacy, proving its superiority. © 2023, The Author(s).

关键词： Sensor nodes

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Optimization of Photovoltaic Power Forecasting: A Comparative Study of Deep Learning Architectures, Optimization Techniques, and Evaluation Metrics 22

Optimization of Photovoltaic Power Forecasting: A Comparativ...

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22nd International Learning and Technology Conference, L and T 2025

作者： Mauladdawilah, Husein Balfaqih, Mohammed Balfagih, Zain Gago, E.J. Pegalajar, M.C. University of Granada School of Civil Engineering Granada Spain University of Jeddah Department of Computer and Network Engineering Jeddah Saudi Arabia Effat University Computer Science Department Jeddah Saudi Arabia University of Granada Department of Computer Science and Artificial Intelligence Granada Spain

ISBN: (纸本)9798331532215

This study presents a systematic evaluation of deep learning architectures for photovoltaic (PV) power forecasting, comparing nine model configurations across three architectures (MLP, LSTM, CNN) and three optimizers (Adam, RMSprop, Adagrad). Using six years of hourly data from a 350kWp grid-connected PV system in Scotland, we demonstrate that architectural choice and optimizer selection significantly impact forecasting accuracy. The LSTM-RMSprop configuration achieved superior performance with RMSE of 2.651 kWh and MAE of 1.197 kWh, showing a 90.29% coefficient of determination (R2). This outperforms both CNN (RMSE: 2.767-2.902 kWh) and MLP architectures (RMSE: 3.104-3.115 kWh) across all optimizers. Our main contributions include: (1) comprehensive optimization of model architectures through hyperparameter evaluation, revealing optimal configurations for each model type;(2) systematic evaluation of optimizer impact, demonstrating RMSprop's superiority for LSTM with improved accuracy across architectures;(3) detailed error analysis showing model stability across different conditions, with NRMSE ranging from 31.15% to 39.07%;and (4) practical insights into computational requirements, where CNN architectures achieve fastest training times (2.3-3.8 minutes/epoch) compared to LSTM (3.9-5.9 minutes/epoch) and MLP (36.54-46.72 minutes/epoch). Results demonstrate that LSTM architectures with appropriate optimization can outperform simpler models in PV power forecasting, providing valuable guidance for practical implementations. © 2025 IEEE.

关键词： Contrastive Learning

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Generative Multimodal Data Augmentation for Low-Resource Multimodal Named Entity Recognition 24

Generative Multimodal Data Augmentation for Low-Resource Mul...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Li, Ziyan Yu, Jianfei Yang, Jia Wang, Wenya Yang, Li Xia, Rui School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China College of Computing and Data Science Nanyang Technological University Singapore Wee Kim Wee School of Communication and Information Nanyang Technological University Singapore

ISBN: (纸本)9798400706868

As an important task in multimodal information extraction, Multimodal Named Entity Recognition (MNER) has recently attracted considerable attention. One key challenge of MNER lies in the lack of sufficient fine-grained annotated data, especially in low-resource scenarios. Although data augmentation is a widely used technique to tackle the above issue, it is challenging to simultaneously generate synthetic text-image pairs and their corresponding high-quality entity annotations. In this work, we propose a novel Generative Multimodal Data Augmentation (GMDA) framework for MNER, which contains two stages: Multimodal Text Generation and Multimodal Image Generation. Specifically, we first transform each annotated sentence into a linearized labeled sequence, and then train a Label-aware Multimodal Large Language Model (LMLLM) to generate the labeled sequence based on a label-aware prompt and its associated image. We further employ a Stable Diffusion model to generate the synthetic images that are semantically related to these sentences. Experimental results on three benchmark datasets demonstrate the effectiveness of the proposed GMDA framework, which consistently boosts the performance of several competitive methods for two subtasks of MNER in both full-supervision and low-resource settings. The low-resource dataset and source code are released at https://***/NUSTM/GMDA. © 2024 ACM.

关键词： data augmentation

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Local binary pattern-based reversible data hiding

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CAAI Transactions on Intelligence Technology 2022年第4期7卷 695-709页

作者： Monalisa Sahu Neelamadhab Padhy Sasanko Sekhar Gantayat Aditya Kumar Sahu Department of Computer Science and Engineering School of Engineering and TechnologyGIET UniversityGunupurOdishaIndia Department of Computer Science and Engineering GMRITRajamAndhra PradeshIndia Department of CSE Vignan's Foundation for Science Technology and ResearchVadlamudiAndhra PradeshIndia

A novel local binary pattern-based reversible data hiding(LBP-RDH)technique has been suggested to maintain a fair symmetry between the perceptual transparency and hiding *** embedding,the image is divided into various 3×3 ***,using the LBP-based image descriptor,the LBP codes for each block are ***,the obtained LBP codes are XORed with the embedding bits and are concealed in the respective blocks using the proposed pixel readjustment ***,each cover image(CI)pixel produces two different stego-image ***,during extraction,the CI pixels are restored without the loss of a single bit of *** outcome of the proposed technique with respect to perceptual transparency measures,such as peak signal-to-noise ratio and structural similarity index,is found to be superior to that of some of the recent and state-of-the-art *** addition,the proposed technique has shown excellent resilience to various stego-attacks,such as pixel difference histogram as well as regular and singular ***,the out-off boundary pixel problem,which endures in most of the contemporary data hiding techniques,has been successfully addressed.

关键词： hiding capacity(HC) local binary pattern(LBP) peak signal-to-noise ratio(PSNR) reversible data hiding

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