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Secure Channel Estimation Using Norm Estimation Model for 5G Next Generation Wireless Networks

computers, Materials & Continua 2025年第1期82卷 1151-1169页

作者： Khalil Ullah Song Jian Muhammad Naeem Ul Hassan Suliman Khan Mohammad Babar Arshad Ahmad Shafiq Ahmad Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China Department of Computer Science Abbottabad University of Science and TechnologyHavelianAbbottabad22500Pakistan Department of Computing and Electronics Engineering Middle East CollegeMuscat124Oman Department of CS&IT Pak-Austria Fachhochschule Institute of Applied Sciences and Technology MangHaripur22621Pakistan Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The emergence of next generation networks(NextG),including 5G and beyond,is reshaping the technological landscape of cellular and mobile *** networks are sufficiently scaled to interconnect billions of users and *** in academia and industry are focusing on technological advancements to achieve highspeed transmission,cell planning,and latency reduction to facilitate emerging applications such as virtual reality,the metaverse,smart cities,smart health,and autonomous *** continuously improves its network functionality to support these *** input multiple output(MIMO)technology offers spectral efficiency,dependability,and overall performance in *** article proposes a secure channel estimation technique in MIMO topology using a norm-estimation model to provide comprehensive insights into protecting NextG network components against adversarial *** technique aims to create long-lasting and secure NextG networks using this extended *** viability of MIMO applications and modern AI-driven methodologies to combat cybersecurity threats are explored in this ***,the proposed model demonstrates high performance in terms of reliability and accuracy,with a 20%reduction in the MalOut-RealOut-Diff metric compared to existing state-of-the-art techniques.

关键词： Next generation networks massive mimo communication network artificial intelligence 5G adversarial attacks channel estimation information security

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Evaluating marginal likelihood approximations of dose–response relationship models in Bayesian benchmark dose methods for risk assessment

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Computational Toxicology 2025年 34卷

作者： Minewaki, Sota Ohigashi, Tomohiro Sozu, Takashi Department of Information and Computer Technology Tokyo University of Science of Engineering Japan Department of Information and Computer Technology Faculty of Engineering Tokyo University of Science Japan

Benchmark dose (BMD;a dose associated with a specified change in response) is used to determine the point of departure for the acceptable daily intake of substances for humans. Multiple dose–response relationship models are considered in the BMD method. The Bayesian model averaging (BMA) method is commonly used, where several models are averaged based on their posterior probabilities, which are determined by calculating the marginal likelihood (ML). Several ML approximation methods are employed in standard software packages, such as BBMD, ToxicR, and the EFSA platform for the BMD method, because the ML cannot be analytically calculated. Although ML values differ among approximation methods, resulting in BMD estimates, this phenomenon is neither widely recognized nor quantitatively evaluated. In this study, we evaluated the agreement of BMD estimates among five ML approximation methods in the BMA method. The five ML approximation methods are (1) maximum likelihood estimation (MLE)-based Schwarz criterion, (2) Markov chain Monte Carlo (MCMC)-based Schwarz criterion, (3) Laplace approximation, (4) density estimation, and (5) bridge sampling. We used eight dose–response relationship models and three prior distributions used in BBMD and ToxicR for 518 experimental datasets. The agreement among the approximation methods tended to be low in the non-informative prior distribution. Although the agreements tended to be high in the informative prior distribution, they were low in some approximation methods. Since the approximation method and the prior distribution affect the agreement, their selection should be carefully considered when implementing BMD methods. © 2025 Elsevier B.V.

关键词： Risk assessment

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Optimal scheduling of graph states via path decompositions

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Physical Review A 2025年第1期111卷 012627-012627页

作者： Samuel J. Elman Jason Gavriel Ryan L. Mann Centre for Quantum Software and Information School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney NSW 2007 Australia Centre for Quantum Computation and Communication Technology Faculty of Engineering and Information Technology University of Technology Sydney NSW 2007 Australia

We study the optimal scheduling of graph states in measurement-based quantum computation, establishing an equivalence between measurement schedules and path decompositions of graphs. We define the spatial cost of a measurement schedule based on the number of simultaneously active qubits and prove that an optimal measurement schedule corresponds to a path decomposition of minimal width. Our analysis shows that approximating the spatial cost of a graph is NP-hard, while for graphs with bounded spatial cost, we establish an efficient algorithm for computing an optimal measurement schedule.

关键词： Quantum computers

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Machine learning for Internet of Things (IoT) device identification: a comparative study

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PeerJ computer Science 2025年 11卷 1-34页

作者： Tahaei, Hamid Liu, Anqi Forooghikian, Hamid Gheisari, Mehdi Zaki, Faiz Anuar, Nor Badrul Fang, Zhaoxi Huang, Longjun Institute of Artificial Intelligence Shaoxing University Shaoxing China School of Computing and Data Science Xiamen University Malaysia Selangor Malaysia Department of Management Islamic Azad University of Kish Kish Iran Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Selangor Kuala Lumpur Malaysia Department of Computer Science and Engineering Shaoxing University Shaoxing China

The rapid deployment of millions of connected devices brings significant security challenges to the Internet of Things (IoT). IoT devices are typically resource-constrained and designed for specific tasks, from which new security challenges are introduced. As such, IoT device identification has garnered substantial attention and is regarded as an initial layer of cybersecurity. One of the major steps in distinguishing IoT devices involves leveraging machine learning (ML) techniques on device network flows known as device fingerprinting. Numerous studies have proposed various solutions that incorporate ML and feature selection (FS) algorithms with different degrees of accuracy. Yet, the domain needs a comparative analysis of the accuracy of different classifiers and FS algorithms to comprehend their true capabilities in various datasets. This article provides a comprehensive performance evaluation of several reputable classifiers being used in the literature. The study evaluates the efficacy of filter-and wrapper-based FS methods across various ML classifiers. Additionally, we implemented a Binary Green Wolf Optimizer (BGWO) and compared its performance with that of traditional ML classifiers to assess the potential of this binary meta-heuristic algorithm. To ensure the robustness of our findings, we evaluated the effectiveness of each classifier and FS method using two widely utilized datasets. Our experiments demonstrated that BGWO effectively reduced the feature set by 85.11% and 73.33% for datasets 1 and 2, respectively, while achieving classification accuracies of 98.51% and 99.8%, respectively. The findings of this study highlight the strong capabilities of BGWO in reducing both the feature dimensionality and accuracy gained through classification. Furthermore, it demonstrates the effectiveness of wrapper methods in the reduction of feature sets. 2025 Tahaei et al.

关键词： Cyber attacks

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Selfish-Free Influential Nodes Detection in Mobile Social Networks 2

Selfish-Free Influential Nodes Detection in Mobile Social Ne...

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2nd International Conference on Advanced Innovations in Smart Cities, ICAISC 2025

作者： Abdelmohsen, Doaa Abdelkader, Tamer Hamdy, Mohamed Elbahnasy, Khaled Ain Shams University Faculty of Computer and Information Sciences Cairo Egypt Galala Unversity Faculty of Computer Science and Engineering Suez Egypt King Abdul Aziz University Faculty of Computing and Information Technology Saudi Arabia Jeddah International College Saudi Arabia

ISBN: (纸本)9798331506995

Mobile networks where users group in communities based on their interests, and disseminate data mainly to their community using their mobile devices, are called Mobile Social Networks (MSN). However, the efficiency of data dissemination becomes a challenging problem in these networks. Using influential users can improve the efficiency of data dissemination. An influential user should have an effective activity on the network and influence other users. The participation behavior of nodes in the network dramatically affects the detection process of influential nodes. Self-sufficient nodes who selectively participate in the dissemination of data in the network may reduce the influence of the other nodes. This paper proposes detecting influential nodes that consider the negative effects of selfish behavior. The performance analysis of the proposed selfish-free influential node detection protocol is presented by comparing it to another one that does not consider the selfishness problem. This comparison shows that the proposed protocol achieves efficiency up to 30% in a environment of selfish behavior. © 2025 IEEE.

关键词： Publishing

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YOLOv8 for Personal Device Screen Detection to Preserve Data Privacy: Enhancing Security Measures in Real-Time Monitoring Systems 10

YOLOv8 for Personal Device Screen Detection to Preserve Data...

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10th International Conference on Digital Arts, Media and technology and 8th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Nimkoompai, Apichaya Nilsiam, Yuenyong Nuchitprasitchai, Siranee Sirikongtham, Puwadol Thai-Nichi Institute of Technology Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Information Technology and Digital Innovation Department of Information Technology Bangkok Thailand

ISBN: (纸本)9798331543273

In public and semi-public environments, personal device usage often exposes sensitive information, presenting a challenge to user privacy when such data is captured by surveillance systems. This study investigates the application of YOLOv8, an advanced object detection model, to identify and obscure personal electronic device screens, particularly mobile phones, in CCTV footage. Utilizing real-time blurring with a Gaussian kernel, this approach ensures that sensitive information displayed on these devices remains unreadable in video footage. The study aims to address the following research questions: (1) Each version of YOLOv8 varies in its effectiveness for detecting and identifying mobile device screens? (2) What level of Gaussian blurring is sufficient to obscure sensitive information without compromising processing efficiency? Experimental results demonstrate that YOLOv8m consistently excels, particularly in precision and mAP50, making it the ideal choice for applications where high accuracy is essential. Meanwhile, YOLOv8n, as the smallest model, delivers promising outcomes in terms of processing speed. Additionally, a 35x35 Gaussian kernel achieves an optimal balance between privacy protection and computational efficiency. These findings support the feasibility of integrating YOLOv8-based real-time blurring into surveillance systems, providing a robust solution for privacy protection in high-risk surveillance areas. © 2025 IEEE.

关键词： Sensitive data

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Privacy Protection in Learning Management Systems’ Mobile technology-Based Learning Analytics

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第6期19卷 197-208页

作者： Rawat, Manoj Kumar Jha, Suresh Kumar Sree Lakshmi, A. Venkata Ramana, J. Gonge, Sudhanshu S. De, Arunava Department of Computer Science and Engineering Medi-Caps University Madhya Pradesh Indore India Manipal University Rajasthan Jaipur India Geetanjali College of Engineering and Technology Telangana Hyderabad India Department of MBA KL Business School Koneru Lakshmaiah Education Foundation Vaddeswaram Campus Guntur District Andhra Pradesh India Department of Computer Science & Information Technology Symbiosis Institute of Technology Lavale A Constituent of Symbiosis International University Maharashtra Pune India Faculty of Information Technology Gopal Narayan Singh University Bihar Jamuhar India

Mobile technology use in education is changing, much like in the business and health sectors. Research is now conducted on designing user-centric platforms that allow people to engage in teaching and learning activities. Although the learning management systems (LMS) field supports students and academic endeavors, the application’s high requirements prevent it from having the desired effect. More significantly, although mobile applications are conve-nient, there is a chance that user data could be maliciously exploited and eavesdropped. Due to the communication overhead of sending raw data to the central learning processor, wire-less application structures cannot adapt to the original structure of mobile learning, which calls for data and processing heads to have a centralized entity. The goal is to offer fresh viewpoints and approaches that bring educational assessment up to date. This project aims to give educators scientifically based assessment tools and tactics by thoroughly examining the use of mobile technology in academic assessment. This will allow for more accurate teaching management and individualized instruction. © 2025 by the authors of this article. Published under CC-BY.

关键词： Mobile computing

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Rethinking Transferable Adversarial Attacks With Double Adversarial Neuron Attribution

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 354-364页

作者： Zhu, Zhiyu Jin, Zhibo Wang, Xinyi Zhang, Jiayu Chen, Huaming Choo, Kim-Kwang Raymond University of Sydney School of Electrical and Computer Engineering DarlingtonNSW2008 Australia University of Malaya Faculty of Computer Science and Information Technology KualaLumpur50603 Malaysia SuZhou Yierqi Jiangsu Suzhou215000 China The University of Texas at San Antonio San AntonioTX78249 United States

Transferable adversarial attacks are a threat to deep neural networks, in particular, for black-box scenarios where access to model information is limited. One can, for example, exploit the intermediate layer neurons to generate transferable adversarial samples. However, current works show limitations in providing feature-level attack mechanisms across multiple victim models. In light of the attribution methods, in this article, we investigate the attribution similarity across different models. We leverage the similarity to incorporate different attribution properties to enhance the sample transferability, for the first time, formulating a novel neuron attribution-based transferable attack termed DANAA++. Specifically, we utilize a range of adversarial attack methods to generate different baseline points through adversarial training. The attribution results are thus obtained along both linear and nonlinear integration paths. In our experiments, the baseline points and integration paths significantly help improve the transferability of adversarial samples. Our approach provides novel insights for building effective attribution-based feature-level adversarial attacks. https://***/LMBTough/DANAAPP. © 2020 IEEE.

关键词： Iterative methods

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Automatic speaker identification system based on MLP network and deep learning in the presence of severe interference

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Multimedia Tools and Applications 2025年 1-42页

作者： Shafik, Amira Sedik, Ahmed El-Shafai, Walid Khalaf, Ashraf A. M. El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering Faculty of Engineering Minia University Minia61519 Egypt Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafr Elsheikh University Kafr El‑Shaikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Automatic Speaker Identification (ASI) is so crucial for security. Current ASI systems perform well in quiet and clean surroundings. However, in noisy situations, the robustness of an ASI system against additive noise and interference is a crucial factor. An investigation of the impact of interference on ASI system performance is presented in this paper, which introduces algorithms for achieving high ASI system performance. The objective is to resist the interference of various forms. This paper presents two models for the ASI task in the presence of interference. The first one depends on Normalized Pitch Frequency (NPF) and Mel-Frequency Cepstral Coefficients (MFCCs) as extracted features and Multi-Layer Perceptron (MLP) as a classifier. In this model, we investigate the utilization of a Discrete Transform (DT), such as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST), to increase the robustness of extracted features against different types of degradation through exploiting the sub-band decomposition characteristics of DWT and the energy compaction property of DCT and DST. This is achieved by extracting features directly from contaminated speech signals in addition to features extracted from discrete transformed signals to create hybrid feature vectors. The enhancement techniques, such as Spectral Subtraction (SS), Winer Filter, and adaptive Wiener filter, are used in a preprocessing stage to eliminate the effect of the interference on the ASI system. In the second model, we investigate the utilization of Deep Learning (DL) based on a Convolutional Neural Network (CNN) with speech signal spectrograms and their Radon transforms to increase the robustness of the ASI system against interference effects. One of this paper goals is to introduce a comparison between the two models and build a more robust ASI system against severe interference. The experimental results indicate that the two proposed models lead to satisfa

关键词： Additive noise

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A Novel Hybrid Approach-Based on Heartbeat and Accelerometer Signals for Enhanced Security in WBSNs

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IEEE Sensors Journal 2025年第6期25卷 10295-10308页

作者： Yan, Guixiang Zhang, Guanghe Liu, Fanghua Williams Samuel, Oluwarotimi Aborokbah, Majed Guo, Jiquan Zhu, Liqing Wei, Sikang Jiangxi Normal University School of Computer and Information Engineering Nanchang330022 China University of Derby School of Computing and Data Science Research Centre DerbyDE22 3AW United Kingdom INTI International University Faculty of Data Science and Information Technology Nilai71800 Malaysia University of Tabuk Faculty of Computers and Information Technology Tabuk71491 Saudi Arabia Jiangxi Normal University School of Digital Industry Shangrao334000 China

In wireless body sensor networks (WBSNs), ensuring secure and efficient key distribution is critical, particularly given the limited computational and energy resources of the sensors. Existing methods often struggle to balance security with these resource constraints, especially in environments involving physiological data such as acceleration (ACC) and electrocardiogram (ECG) signals. For the first time, this study proposes a novel hybrid approach that integrates fuzzy commitment with ACC signal noise and ECG features for efficient key generation and distribution in WBSNs. By employing a low-pass filter to process ACC signal noise, we generated highly random binary sequences (BSs), leveraging the inherent randomness of the signal for secure key generation. Concurrently, an optimized coding scheme was built for ECG feature construction to ensure secure key distribution between devices. Extensive experiments, including entropy analysis and National Institute of Standards and technology (NIST) statistical tests, confirm the robustness and security of our method. The proposed scheme achieves a false acceptance rate (FAR) of 5.23%, demonstrating superior performance across multiple databases in comparison to benchmark approaches. This novel dual-key generation strategy that combines the unpredictability of ACC noise with the individual-specific traits of ECG signals can significantly enhance the security, applicability, and versatility of WBSNs. © 2001-2012 IEEE.

关键词： Acceptance tests

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