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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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Prediction of software quality with Machine Learning-Based ensemble methods

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Materials Today: Proceedings 2023年 81卷 18-25页

作者： Ceran, A. Ayberk Ar, Yilmaz Tanrıöver, Ö. Özgür Seyrek Ceran, Sinem Department of Computer Engineering Ankara University Ankara 06830 Turkey Department of Software Engineering Turkish Aeronautical Association University Ankara 06790 Turkey

software quality prediction is used at various stages of projects. There are several metrics that provide the quality measure with respect to different types of software. In this study, defect density is used as the feature that represents the quality. The aim of this study is to predict software quality with higher accuracy than previous studies. The study shows that data pre-processing, feature extraction and machine learning algorithms provide more accurate results in predicting the quality of the software. Algorithms were applied on three datasets that include software metrics. The obtained accuracy values were compared with the existing ones in the literature. Ensemble methods, which enable the use of different machine learning algorithms together, contributed to obtaining results with higher accuracy rates in software quality prediction. In EBSPM dataset, logistic regression produced the best accuracy value as 96.67%. On the other hand, in ISBSG dataset and in PROMISE dataset, the ensemble methods Soft Voting & Stacking and Gradient Boosting & Bagging generated the best results respectively with the values 96.31% and 94.59%. © 2022

关键词： Machine learning

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Scaling Attacks on Large Logic-Locked Designs

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2023年第6期44卷 1-1页

作者： Moosa, Abdul Khader Thalakkattu Tan, Benjamin Karri, Ramesh Department of Electrical and Computer Engineering New York University New York USA Department of Electrical and Software Engineering University of Calgary Alberta Canada

Researchers have developed numerous strategies to alleviate the threat of malicious third-party foundries, including logic locking and its numerous sophisticated variants for hardware intellectual property (IP) protection. Recent work at the register-transfer level has opened the door to “large-scale” locking of large IPs (comprising thousands of gates) with hundreds to thousands of key bits. Recent security evaluation of such techniques treats the locked design as a monolith and has suggested that large logic-locked designs are practically secure, even from powerful SAT-based attacks. In this work, we challenge such findings by proposing and evaluating a novel algorithmic method to de-obfuscate large logic-locked circuits by attacking a set of small sub-circuit cones. The algorithm chooses a sub-optimal set of sub-circuit cones and proposes an attack sequence on these cones by leveraging the observation that each locking key-bit is distributed across multiple sub-circuit cones of varying sizes. This Divide And Conquer SAT (DACSAT) attack framework can de-obfuscate large designs, like an AES IP comprising 300,000 gates, logic-locked with up to 50,000 keys in around 3600 seconds, while an out-of-the-box, state-of-the-art SAT attack tool fails. IEEE

关键词： Flip flop circuits

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Improving Brain Tumor Classification with Deep Learning Using Synthetic Data

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computers, Materials & Continua 2023年第3期74卷 5049-5067页

作者： Muhammed Mutlu Yapici Rukiye Karakis Kali Gurkahraman Elmadag Vocational School Department of Computer TechnologiesAnkara UniversityAnkaraTurkey Faculty of Technology Department of Software EngineeringSivas Cumhuriyet UniversitySivas58140Turkey Faculty of Engineering Department of Computer EngineeringSivas Cumhuriyet UniversitySivas58140Turkey

Deep learning(DL)techniques,which do not need complex preprocessing and feature analysis,are used in many areas of medicine and achieve promising *** the other hand,in medical studies,a limited dataset decreases the abstraction ability of the DL *** this context,we aimed to produce synthetic brain images including three tumor types(glioma,meningioma,and pituitary),unlike traditional data augmentation methods,and classify them with *** study proposes a tumor classification model consisting of a Dense Convolutional Network(DenseNet121)-based DL model to prevent forgetting problems in deep networks and delay information flow between *** comparing models trained on two different datasets,we demonstrated the effect of synthetic images generated by Cycle Generative Adversarial Network(CycleGAN)on the generalization of *** model is trained only on the original dataset,while the other is trained on the combined dataset of synthetic and original *** data generated by CycleGAN improved the best accuracy values for glioma,meningioma,and pituitary tumor classes from 0.9633,0.9569,and 0.9904 to 0.9968,0.9920,and 0.9952,*** developed model using synthetic data obtained a higher accuracy value than the related studies in the ***,except for pixel-level and affine transform data augmentation,synthetic data has been generated in the figshare brain dataset for the first time.

关键词： Brain tumor classification deep learning cycle generative adversarial network data augmentation

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Integration of Federated Learning and Graph Convolutional Networks for Movie Recommendation Systems

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computers, Materials & Continua 2025年第5期83卷 2041-2057页

作者： Sony Peng Sophort Siet Ilkhomjon Sadriddinov Dae-Young Kim Kyuwon Park Doo-Soon Park Department of Software Convergence Soonchunhyang UniversityAsan31538Republic of Korea Department of Computer Software and Engineering Soonchunhyang UniversityAsan31538Republic of Korea AI⋅SW Education Institute Soonchunhyang UniversityAsan31538Republic of Korea

Recommendation systems(RSs)are crucial in personalizing user experiences in digital environments by suggesting relevant content or *** filtering(CF)is a widely used personalization technique that leverages user-item interactions to generate ***,it struggles with challenges like the cold-start problem,scalability issues,and data *** address these limitations,we develop a Graph Convolutional Networks(GCNs)model that captures the complex network of interactions between users and items,identifying subtle patterns that traditional methods may *** integrate this GCNs model into a federated learning(FL)framework,enabling themodel to learn fromdecentralized *** not only significantly enhances user privacy—a significant improvement over conventionalmodels but also reassures users about the safety of their ***,by securely incorporating demographic information,our approach further personalizes recommendations and mitigates the coldstart issue without compromising user *** validate our RSs model using the openMovieLens dataset and evaluate its performance across six key metrics:Precision,Recall,Area Under the Receiver Operating Characteristic Curve(ROC-AUC),F1 Score,Normalized Discounted Cumulative Gain(NDCG),and Mean Reciprocal Rank(MRR).The experimental results demonstrate significant enhancements in recommendation quality,underscoring that combining GCNs with CF in a federated setting provides a transformative solution for advanced recommendation systems.

关键词： Recommendation systems collaborative filtering graph convolutional networks federated learning framework

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Redundant Transmission Control Algorithm for Information-Centric Vehicular IoT Networks

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computers, Materials & Continua 2023年第8期76卷 2217-2234页

作者： Abdur Rashid Sangi Satish Anamalamudi Mohammed SAlkatheiri Murali Krishna Enduri Anil Carie Mohammed AAlqarni Department of Computer Science College of Science and TechnologyWenzhou-Kean UniversityWenzhou325060China Department of Computer Science Engineering SRM University APAmaravati522502India Department of Cybersecurity College of Computer Science&EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Software Engineering College of Computer Science&EngineeringUniversity of JeddahJeddahSaudi Arabia

Vehicular Adhoc Networks(VANETs)enable vehicles to act as mobile nodes that can fetch,share,and disseminate information about vehicle safety,emergency events,warning messages,and passenger ***,the continuous dissemination of information fromvehicles and their one-hop neighbor nodes,Road Side Units(RSUs),and VANET infrastructures can lead to performance degradation of VANETs in the existing hostcentric IP-based ***,Information Centric Networks(ICN)are being explored as an alternative architecture for vehicular communication to achieve robust content distribution in highly mobile,dynamic,and errorprone *** ICN-based Vehicular-IoT networks,consumer mobility is implicitly supported,but producer mobility may result in redundant data transmission and caching inefficiency at intermediate vehicular *** paper proposes an efficient redundant transmission control algorithm based on network coding to reduce data redundancy and accelerate the efficiency of information *** proposed protocol,called Network Cording Multiple Solutions Scheduling(NCMSS),is receiver-driven collaborative scheduling between requesters and information sources that uses a global parameter expectation deadline to effectively manage the transmission of encoded data packets and control the selection of information *** results for the proposed NCMSS protocol is demonstrated to analyze the performance of ICN-vehicular-IoT networks in terms of caching,data retrieval delay,and end-to-end application *** end-to-end throughput in proposed NCMSS is 22%higher(for 1024 byte data)than existing solutions whereas delay in NCMSS is reduced by 5%in comparison with existing solutions.

关键词： Caching data dissemination redundancy control ICN-vehicular IoT networks

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Aspect Extraction Approach for Sentiment Analysis Using Keywords

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computers, Materials & Continua 2023年第3期74卷 6879-6892页

作者： Nafees Ayub Muhammad Ramzan Talib Muhammad Kashif Hanif Muhammad Awais Department of Computer Science Government College UniversityFaisalabad38000Pakistan Department of Software Engineering Government College UniversityFaisalabad38000Pakistan

Sentiment Analysis deals with consumer reviews available on blogs,discussion forums,E-commerce websites,andApp *** online reviews about products are also becoming essential for consumers and companies as *** rely on these reviews to make their decisions about products and companies are also very interested in these reviews to judge their products and *** reviews are also a very precious source of information for requirement *** companies and consumers are not very satisfied with the overall sentiment;they like fine-grained knowledge about consumer *** to this,many researchers have developed approaches for aspect-based sentiment *** existing approaches concentrate on explicit aspects to analyze the sentiment,and only a few studies rely on capturing implicit *** paper proposes a Keywords-Based Aspect Extraction method,which captures both explicit and implicit *** also captures opinion words and classifies the sentiment about each *** applied semantic similarity-basedWordNet and SentiWordNet lexicon to improve aspect *** used different collections of customer reviews for experiment purposes,consisting of eight datasets over seven *** compared our approach with other state-of-the-art approaches,including Rule Selection using Greedy Algorithm(RSG),Conditional Random Fields(CRF),Rule-based Extraction(RubE),and Double Propagation(DP).Our results have shown better performance than all of these approaches.

关键词： Sentiment analysis aspect extraction keywords-based machine learning

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An Empirical Study on Small-Sized Datasets Based on Eubank’s Optimal Spacing Theorem

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SN computer Science 2025年第1期6卷 1-19页

作者： Abedu, Samuel Mensah, Solomon Boafo, Frederick Department of Computer Science and Software Engineering Concordia University Montreal Canada Department of Computer Science University of Ghana Accra Ghana Department of Computer Science Lancaster University Ghana Accra Ghana

Conventional machine learning methods for software effort estimation (SEE) have seen an increase in research interest. Conversely, there are few research that try to evaluate how well deep learning techniques work in SEE. This can be attributed to the relatively small sizes of SEE datasets. The goal of the study is to establish a threshold for small-sized datasets in SEE. Additionally, it looks into how well certain deep learning and traditional machine learning models perform on small-sized datasets. From the body of existing literature, plausible SEE datasets are extracted and ranked along with their attributes and number of project cases. The ranking of the project instances is discretized into three classes (small, medium, and large) using Eubank’s optimal spacing theory. Using the leave-one-out cross-validation, each small-sized dataset is used to train two deep learning models and five conventional machine learning models. Each model’s ability to make predictions is evaluated using its mean absolute error. Results show that, on small-scale datasets, deep learning models outperform traditional machine learning models in terms of prediction accuracy, which contradicts what is previously known. Regularisation techniques can be used in conjunction with deep learning to address SEE. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Eubank’s optimal spacing theory Small-sized software effort estimation Traditional Machine learning

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Efficient Bit-Plane Based Medical Image Cryptosystem Using Novel and Robust Sine-Cosine Chaotic Map

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computers, Materials & Continua 2025年第4期83卷 917-933页

作者： Zeric Tabekoueng Njitacke Louai A.Maghrabi Musheer Ahmad Turki Althaqafi Department of Electrical and Electronic Engineering College of Technology(COT)University of BueaBueaP.O.Box 63Cameroon Department of Software Engineering College of EngineeringUniversity of Business and TechnologyJeddah22246Saudi Arabia Department of Computer Engineering Jamia Millia IslamiaNew Delhi110025India Department of Computer Science School of EngineeringComputing and DesignDar Al-Hekma UniversityJeddah22246Saudi Arabia

This paper presents a high-security medical image encryption method that leverages a novel and robust sine-cosine *** map demonstrates remarkable chaotic dynamics over a wide range of *** employ nonlinear analytical tools to thoroughly investigate the dynamics of the chaotic map,which allows us to select optimal parameter configurations for the encryption *** findings indicate that the proposed sine-cosine map is capable of generating a rich variety of chaotic attractors,an essential characteristic for effective *** encryption technique is based on bit-plane decomposition,wherein a plain image is divided into distinct bit *** planes are organized into two matrices:one containing the most significant bit planes and the other housing the least significant *** subsequent phases of chaotic confusion and diffusion utilize these matrices to enhance *** auxiliary matrix is then generated,comprising the combined bit planes that yield the final encrypted *** results demonstrate that our proposed technique achieves a commendable level of security for safeguarding sensitive patient information in medical *** a result,image quality is evaluated using the Structural Similarity Index(SSIM),yielding values close to zero for encrypted images and approaching one for decrypted ***,the entropy values of the encrypted images are near 8,with a Number of Pixel Change Rate(NPCR)and Unified Average Change Intensity(UACI)exceeding 99.50%and 33%,***,quantitative assessments of occlusion attacks,along with comparisons to leading algorithms,validate the integrity and efficacy of our medical image encryption approach.

关键词： Image cryptosystem robust chaos sine-cosine map nonlinear analysis tools medical images

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Framework for Effective Utilization of Distributed Scrum in software Projects

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computer Systems Science & engineering 2023年第1期44卷 407-422页

作者： Basit Shahzad Wardah Naeem Awan Fazal-e-Amin Ahsanullah Abro Muhammad Shoaib Sultan Alyahya Department of Software Engineering National University of Modern LanguagesIslamabad44000Pakistan Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science Sukkur IBA UniversitySindh65200Pakistan Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

There is an emerging interest in using agile methodologies in Global software Development(GSD)to get the mutual benefits of both *** is currently admired by many development teams as an agile most known meth-odology and considered adequate for collocated *** the same time,stake-holders in GSD are dispersed by geographical,temporal,and socio-cultural *** to the controversial nature of Scrum and GSD,many significant challenges arise that might restrict the use of Scrum in *** conducted a Sys-tematic Literature Review(SLR)by following Kitchenham guidelines to identify the challenges that limit the use of Scrum in GSD and to explore the mitigation strategies adopted by practitioners to resolve the *** validate our reviewfindings,we conducted an industrial survey of 305 *** results of our study are consolidated into a research *** framework represents current best practices and recommendations to mitigate the identified distributed scrum challenges and is validated byfive experts of distributed *** of the expert review were found supportive,reflecting that the framework will help the stakeholders deliver sustainable products by effectively mitigating the identified challenges.

关键词： Global software development distributed Scrum sustainable development challenges mitigation strategies

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