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Application of Physical Unclonable Function for Lightweight Authentication in Internet of Things

computers, Materials & Continua 2023年第4期75卷 1901-1918页

作者： Ahmad O.Aseeri Sajjad Hussain Chauhdary Mohammed Saeed Alkatheiri Mohammed A.Alqarni Yu Zhuang Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Cybersecurity College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Computer Science Texas Tech UniversityLubbockUSA

IoT devices rely on authentication mechanisms to render secure message *** data transmission,scalability,data integrity,and processing time have been considered challenging aspects for a system constituted by IoT *** application of physical unclonable functions(PUFs)ensures secure data transmission among the internet of things(IoT)devices in a simplified network with an efficient time-stamped *** paper proposes a secure,lightweight,cost-efficient reinforcement machine learning framework(SLCR-MLF)to achieve decentralization and security,thus enabling scalability,data integrity,and optimized processing time in IoT *** has been integrated into SLCR-MLF to improve the security of the cluster head node in the IoT platform during transmission by providing the authentication service for device-to-device *** IoT network gathers information of interest from multiple cluster members selected by the proposed *** addition,the software-defined secured(SDS)technique is integrated with SLCR-MLF to improve data integrity and optimize processing time in the IoT *** analysis shows that the proposed framework outperforms conventional methods regarding the network’s lifetime,energy,secured data retrieval rate,and performance *** enabling the proposed framework,number of residual nodes is reduced to 16%,energy consumption is reduced by up to 50%,almost 30%improvement in data retrieval rate,and network lifetime is improved by up to 1000 msec.

关键词： Cyber-physical systems security data aggregation Internet of Things physical unclonable function swarm intelligences

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Oversampling-Enhanced Feature Fusion-Based Hybrid ViT-1DCNN Model for Ransomware Cyber Attack Detection

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computer Modeling in engineering & Sciences 2025年第2期142卷 1667-1695页

作者： Muhammad Armghan Latif Zohaib Mushtaq Saifur Rahman Saad Arif Salim Nasar Faraj Mursal Muhammad Irfan Haris Aziz Department of Computer and Information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl Ahsa31982Saudi Arabia Department of Mechanical Industrial and Energy System EngineeringUniversity of SargodhaSargodha40100Pakistan

Ransomware attacks pose a significant threat to critical infrastructures,demanding robust detection *** study introduces a hybrid model that combines vision transformer(ViT)and one-dimensional convolutional neural network(1DCNN)architectures to enhance ransomware detection *** common challenges in ransomware detection,particularly dataset class imbalance,the synthetic minority oversampling technique(SMOTE)is employed to generate synthetic samples for minority class,thereby improving detection *** integration of ViT and 1DCNN through feature fusion enables the model to capture both global contextual and local sequential features,resulting in comprehensive ransomware *** on the UNSW-NB15 dataset,the proposed ViT-1DCNN model achieved 98%detection accuracy with precision,recall,and F1-score metrics surpassing conventional *** approach not only reduces false positives and negatives but also offers scalability and robustness for real-world cybersecurity *** results demonstrate the model’s potential as an effective tool for proactive ransomware detection,especially in environments where evolving threats require adaptable and high-accuracy solutions.

关键词： Ransomware attacks cybersecurity vision transformer convolutional neural network feature fusion encryption threat detection

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How far are we to GPT-4V?Closing the gap to commercial multimodal models with open-source suites

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Science China(information Sciences) 2024年第12期67卷 5-22页

作者： Zhe CHEN Weiyun WANG Hao TIAN Shenglong YE Zhangwei GAO Erfei CUI Wenwen TONG Kongzhi HU Jiapeng LUO Zheng MA Ji MA Jiaqi WANG Xiaoyi DONG Hang YAN Hewei GUO Conghui HE Botian SHI Zhenjiang JIN Chao XU Bin WANG Xingjian WEI Wei LI Wenjian ZHANG Bo ZHANG Pinlong CAI Licheng WEN Xiangchao YAN Min DOU Lewei LU Xizhou ZHU Tong LU Dahua LIN Yu QIAO Jifeng DAI Wenhai WANG State Key Laboratory for Novel Software Technology Nanjing University Shanghai AI Laboratory School of Computer Science Fudan University SenseTime Research Department of Information Engineering The Chinese University of Hong Kong Department of Electronic Engineering Tsinghua University

In this paper, we introduce InternVL 1.5, an open-source multimodal large language model(MLLM) to bridge the capability gap between open-source and proprietary commercial models in multimodal understanding. We introduce three simple improvements.(1) Strong vision encoder: we explored a continuous learning strategy for the large-scale vision foundation model — InternViT-6B, boosting its visual understanding capabilities, and making it can be transferred and reused in different LLMs.(2) Dynamic high-resolution: we divide images into tiles ranging from 1 to 40 of 448×448 pixels according to the aspect ratio and resolution of the input images, which supports up to 4K resolution input.(3) High-quality bilingual dataset: we carefully collected a high-quality bilingual dataset that covers common scenes, document images,and annotated them with English and Chinese question-answer pairs, significantly enhancing performance in optical character recognition(OCR) and Chinese-related tasks. We evaluate InternVL 1.5 through a series of benchmarks and comparative studies. Compared to both open-source and proprietary commercial models, InternVL 1.5 shows competitive performance, achieving state-of-the-art results in 8 of 18 multimodal benchmarks. Code and models are available at https://***/OpenGVLab/InternVL.

关键词： multimodal model open-source vision encoder dynamic resolution bilingual dataset

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AMachine Learning Approach to User Profiling for Data Annotation of Online Behavior

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computers, Materials & Continua 2024年第2期78卷 2419-2440页

作者： Moona Kanwal Najeed AKhan Aftab A.Khan Computer Science and Information Technology Department NED University of Engineering and TechnologyKarachiPakistan Computer Engineering Department Sir Syed University of Engineering and TechnologyKarachiPakistan College of Education Health and Human ServicesLongwood UniversityFarmvilleVAUSA

The user’s intent to seek online information has been an active area of research in user *** profiling considers user characteristics,behaviors,activities,and preferences to sketch user intentions,interests,and *** user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content *** user’s complete online experience in seeking information is a blend of activities such as searching,verifying,and sharing it on social ***,a combination of multiple behaviors in profiling users has yet to be *** research takes a novel approach and explores user intent types based on multidimensional online behavior in information *** research explores information search,verification,and dissemination behavior and identifies diverse types of users based on their online engagement using machine *** research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data *** feedback is based on online behavior and practices collected by using a survey *** participants include both males and females from different occupation sectors and different *** data collected is subject to feature engineering,and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their *** techniques are evaluated,and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of *** average is computed to identify user intent type *** user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%.This research successfully extracts different user types based on th

关键词： User intent cluster user profile online search information sharing user behavior search reasons

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A Fault Diagnosis Method for Smart Meters via Two-layer Stacking Ensemble Optimization and Data Augmentation

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Journal of Modern Power Systems and Clean Energy 2024年第4期12卷 1272-1284页

作者： Leijiao Ge Tianshuo Du Zhengyang Xu Luyang Hou Jun Yan Yuanliang Li School of Electrical and Information Engineering Tianjin UniversityTianjin 300072China School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and TelecommunicationsBeijing 100876China Concordia Institute for Information Systems Engineering Concordia UniversityMontrealQC H3G 1M8Canada

The accurate identification of smart meter(SM)fault types is crucial for enhancing the efficiency of operationand maintenance(O&M)and the reliability of power ***,the intelligent classification of SM fault typesfaces significant challenges owing to the complexity of featuresand the imbalance between fault *** address these issues,this study presents a fault diagnosis method for SM incorporatingthree distinct *** first module employs acombination of standardization,data imputation,and featureextraction to enhance the data quality,thereby facilitating improvedtraining and learning by the *** enhance theclassification performance,the data imputation method considersfeature correlation measurement and sequential imputation,and the feature extractor utilizes the discriminative enhancedsparse *** tackle the interclass imbalance of datawith discrete and continuous features,the second module introducesan assisted classifier generative adversarial network,which includes a discrete feature generation ***,anovel Stacking ensemble classifier for SM fault diagnosis is *** contrast to previous studies,we construct a two-layerheuristic optimization framework to address the synchronousdynamic optimization problem of the combinations and hyperparametersof the Stacking ensemble classifier,enabling betterhandling of complex classification tasks using SM *** proposedfault diagnosis method for SM via two-layer stacking ensembleoptimization and data augmentation is trained and validatedusing SM fault data collected from 2010 to 2018 in Zhejiang Province,*** results demonstrate the effectivenessof the proposed method in improving the accuracyof SM fault diagnosis,particularly for minority classes.

关键词： Data augmentation fault diagnosis feature extraction smart meter Stacking ensemble optimization

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An Efficient and Fast Area Optimization Approach for Mixed Polarity Reed-Muller Logic Circuits

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Chinese Journal of Electronics 2024年第5期33卷 1165-1180页

作者： Yuhao ZHOU Zhenxue HE Jianhui JIANG Xiaojun ZHAO Fan ZHANG Limin XIAO Xiang WANG School of Software Engineering Tongji University Key Laboratory of Agricultural Big Data of Hebei Province Hebei Agricultural University School of Computer Science and Engineering Beihang University School of Electronic and Information Engineering Beihang University

Area has become one of the main bottlenecks restricting the development of integrated circuits. The area optimization approaches of existing XNOR/OR-based mixed polarity Reed-Muller(MPRM) circuits have poor optimization effect and efficiency. Given that the area optimization of MPRM logic circuits is a combinatorial optimization problem, we propose a whole annealing adaptive bacterial foraging algorithm(WAA-BFA), which includes individual evolution based on Markov chain and Metropolis acceptance criteria, and individual mutation based on adaptive probability. To address the issue of low conversion efficiency in existing polarity conversion approaches, we introduce a fast polarity conversion algorithm(FPCA). Moreover, we present an MPRM circuits area optimization approach that uses the FPCA and WAA-BFA to search for the best polarity corresponding to the minimum circuits area. Experimental results demonstrate that the proposed MPRM circuits area optimization approach is effective and can be used as a promising EDA tool.

关键词： Integrated circuits Microorganisms Annealing Logic circuits Circuits Benchmark testing Delays

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Implementation of Deep Learning Techniques for Deepfake Classification: A comparative study using ResNet-50 and VGG16 7

Implementation of Deep Learning Techniques for Deepfake Clas...

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Yesugade, Kiran Jadhav, Rohini College of Engineering Department of Computer Engineering Pune India College of Engineering Department of Information Technology Pune India

ISBN: (纸本)9798331527822

This work investigates the use and assessment of ResNet-50 and VGG16 deep learning models for detecting deepfake images using the Labelled Faces in the Wild (LFW) dataset. The methodology encompassed thorough preparation of the dataset and the utilisation of both models to differentiate between authentic and altered facial photos. The models' performance was evaluated using fundamental criteria including accuracy, precision, recall, and F1 score. VGG16 demonstrated superior performance compared to ResNet-50, achieving an accuracy of 93.87%, recall of 96.23%, and an F1 score of 94.41%, while ResNet-50 achieved a precision of 92.54%, recall of 95.26%, and F1 score of 93.88%. Although both models achieved good accuracy, VGG16 had superior stability and generalization across the whole dataset. The study determines that VGG16 is the more dependable model for detecting deepfakes on the LFW dataset. It suggests that future efforts should concentrate on enhancing ResNet-50 or creating ensemble methods to enhance detection capabilities. © 2024 IEEE.

关键词： Contrastive Learning

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The Challenges and Opportunities of Smart Cities Infrastructure

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SN computer Science 2024年第6期5卷 713页

作者： Abuali, Amer Alharthi, Wejdan Althbyani, Mariam Ghabban, Fahad Ameerbakhsh, Omair Alfadli, Ibrahim Al-Shehri, Ashwaq Hasen Fallatah, Najmah Adel College of Computer Science and Engineering Information System Department Taibah University

information communications technology (ICT) refers to the technology used for communication and managing information must be utilized to enhance the environment, economics, mobility, and governance, among other facets of urban life, in order to create smart cities. However this progress is frequently accompanied by a number of challenges and unfavorable outcomes. This study aims to uncover ICT difficulties related to smart city infrastructure through an extensive literature review. In addition, a survey was carried out among Saudi nationals to find out what they thought about, anticipated, and worried about in terms of smart city concepts and features. The study also looked at a literature checklist of ICT problems, which included dangers to information security, incompatibilities across systems, privacy concerns, and deficiencies in digital capabilities. Based on the findings it appears that privacy breaches and information security concerns are the most important issues. This is explained by heightened susceptibility and potential, cyberattacks and a pervasive ignorance of the protection of personal data. Because of the anticipated high costs and difficulties with adaption and utilization, the public is concerned about incompatibility between systems and services. Furthermore, older individuals and those with lesser educational achievement have different digital skills. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Compatibility ICT challenges information insecurity Privacy Smart city Technology skills

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Nonlinear Teager-Kaiser Infomax Boost Clustering Algorithm for Brain Tumor Detection Technique

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computer Systems Science & engineering 2023年第8期46卷 2589-2599页

作者： P.M.Siva Raja S.Brinthakumari K.Ramanan Computer Science and Engineering Amrita College of Engineering and TechnologyNagercoil629901India Information Technology K.C College of Engineering and Management Studies and ResearchThaneIndia Computer Science and Engineering NPR College of Engineering and TechnologyDindigul629902India

Brain tumor detection and division is a difficult tedious undertaking in clinical image *** it comes to the new technology that enables accurate identification of the mysterious tissues of the brain,magnetic resonance imaging(MRI)is a great *** is possible to alter the tumor’s size and shape at any time for any number of patients by using the Brain *** have a difficult time sorting and classifying tumors from multiple *** tumors may be accurately detected using a new approach called Nonlinear Teager-Kaiser Iterative Infomax Boost Clustering-Based Image Segmentation(NTKFIBC-IS).Teager-Kaiser filtering is used to reduce noise artifacts and improve the quality of images before they are *** clinical characteristics are then retrieved and analyzed statistically to identify brain *** use of a BraTS2015 database enables the proposed approach to be used for both qualitative and quantitative *** dataset was used to do experimental evaluations on several metrics such as peak signal-to-noise ratios,illness detection accuracy,and false-positive rates as well as disease detection time as a function of a picture *** segmentation delivers greater accuracy in detecting brain tumors with minimal time consumption and false-positive rates than current stateof-the-art approaches.

关键词： Brain tumor detection image segmentation nonlinear teager kaiser filtering

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Multi‐feature fusion and memory‐based mobile robot target tracking system

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IET Cyber-Systems and Robotics 2024年第3期6卷 62-73页

作者： Hanqing Sun Shijie Zhang Qingle Quan College of Information Engineering Henan University of Animal Husbandry and EconomyZhengzhouHenanChina College of Information Technology and Computer Science University of the CordillerasBaguioPhilippines College of Electrical Engineering Henan University of TechnologyZhengzhouHenanChina

In crowded settings,mobile robots face challenges like target disappearance and occlusion,impacting tracking *** existing optimisations,tracking in complex environments remains *** address this issue,the authors propose a tailored visual navigation tracking system for crowded *** target disappearance,an autonomous navigation strategy based on target coordinates,utilising a path memory bank for intelligent search and re‐tracking is *** significantly enhances tracking *** handle target occlusion,the system relies on appearance features extracted by a target detection network and a feature memory bank for enhanced *** control technology ensures robust target tracking by fully utilising appearance information and motion characteristics,even in occluded *** testing on the OTB100 dataset validates the system's effectiveness in addressing target tracking challenges in diverse crowded environments,affirming algorithm robustness.

关键词： autonomous navigation Kalman filtering memory bank mobile robots proportional‐integral‐derivative

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