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Secure and Efficient Data Transmission Scheme Based on Physical Mechanism

computers, Materials & Continua 2023年第5期75卷 3589-3605页

作者： Ping Zhang Haoran Zhu Wenjun Li Osama Alfarraj Amr Tolba Gwang-jun Kim School of Computer Science Hunan University of Technology and BusinessChangsha410205China School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410004China Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer Engineering Chonnam National UniversityGwangju61186Korea

Many Internet of things application scenarios have the characteristics of limited hardware resources and limited energy supply,which are not suitable for traditional security *** security technology based on the physicalmechanism has attracted extensive *** to improve the key generation rate has always been one of the urgent problems to be solved in the security technology based on the physical *** this paper,superlattice technology is introduced to the security field of Internet of things,and a high-speed symmetric key generation scheme based on superlattice for Internet of things is *** order to ensure the efficiency and privacy of data transmission,we also combine the superlattice symmetric key and compressive sensing technology to build a lightweight data transmission scheme that supports data compression and data encryption at the same *** analysis and experimental evaluation results show that the proposed scheme is superior to the most closely related work.

关键词： Data transmission key generation data privacy compressive sensing Internet of Things(IoT)

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Concept Drift Analysis and Malware Attack Detection System Using Secure Adaptive Windowing

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computers, Materials & Continua 2023年第5期75卷 3743-3759页

作者： Emad Alsuwat Suhare Solaiman Hatim Alsuwat Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif26571Saudi Arabia Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah24382Saudi Arabia

Concept drift is a main security issue that has to be resolved since it presents a significant barrier to the deployment of machine learning(ML)*** to attackers’(and/or benign equivalents’)dynamic behavior changes,testing data distribution frequently diverges from original training data over time,resulting in substantial model *** to their dispersed and dynamic nature,distributed denial-of-service attacks pose a danger to cybersecurity,resulting in attacks with serious consequences for users and *** paper proposes a novel design for concept drift analysis and detection of malware attacks like Distributed Denial of Service(DDOS)in the *** goal of this architecture combination is to accurately represent data and create an effective cyber security prediction *** intrusion detection system and concept drift of the network has been analyzed using secure adaptive windowing with website data authentication protocol(SAW_WDA).The network has been analyzed by authentication protocol to avoid malware *** data of network users will be collected and classified using multilayer perceptron gradient decision tree(MLPGDT)*** on the classification output,the decision for the detection of attackers and authorized users will be *** experimental results show output based on intrusion detection and concept drift analysis systems in terms of throughput,end-end delay,network security,network concept drift,and results based on classification with regard to accuracy,memory,and precision and F-1 score.

关键词： Concept drift machine learning DDOS cyber security SAW_WDA MLPGDT

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Design of an Efficient and Provable Secure Key Exchange Protocol for HTTP Cookies

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computers, Materials & Continua 2024年第7期80卷 263-280页

作者： Waseem Akram Khalid Mahmood Hafiz Burhan ul Haq Muhammad Asif Shehzad Ashraf Chaudhry Taeshik Shon Graduate School of Engineering Science and Technology National Yunlin University of Science and TechnologyYunlin64002TaiwanChina Future Technology Research Center National Yunlin University of Science and TechnologyYunlin64002TaiwanChina Department of Computer Science Lahore Garrison UniversityLahore54920Pakistan Department of Computer Science and Information Technology College of EngineeringAbu Dhabi UniversityAbu Dhabi69911United Arab Emirates Department of Software Engineering Faculty of Engineering and ArchitectureNisantasi UniversityIstanbul34398Turkey Department of Cybersecurity Ajou UniversitySuwon16499Republic of Korea

Cookies are considered a fundamental means of web application services for authenticating various Hypertext Transfer Protocol(HTTP)requests andmaintains the states of clients’information over the *** cookies are exploited to carry client patterns observed by a *** client patterns facilitate the particular client’s future visit to the corresponding ***,security and privacy are the primary concerns owing to the value of information over public channels and the storage of client information on the *** protocols have been introduced that maintain HTTP cookies,but many of those fail to achieve the required security,or require a lot of resource *** this article,we have introduced a lightweight Elliptic Curve Cryptographic(ECC)based protocol for authenticating client and server transactions to maintain the privacy and security of HTTP *** proposed protocol uses a secret key embedded within a *** proposed protocol ismore efficient and lightweight than related protocols because of its reduced computation,storage,and communication ***,the analysis presented in this paper confirms that proposed protocol resists various known attacks.

关键词： Cookies authentication protocol impersonation attack ECC

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Domain-independent adaptive histogram-based features for pomegranate fruit and leaf diseases classification

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CAAI Transactions on Intelligence technology 2025年第2期10卷 317-336页

作者： Mohanmuralidhar Prajwala Prabhuswamy Prajwal Kumar Shanubhog Maheshwarappa Gopinath Shivakumara Palaiahnakote Mahadevappa Basavanna Daniel P.Lopresti Department of Studies in Bio-Technology Davanagere UniversityDavangereKarnatakaIndia Department of Studies in Computer Science Davangere UniversityDavangereKarnatakaIndia School of Science Engineering and EnvironmentUniversity of SalfordSalfordUK Lehigh University BethlehemPennsylvaniaUSA

Disease identification for fruits and leaves in the field of agriculture is important for estimating production,crop yield,and earnings for *** the specific case of pomegranates,this is challenging because of the wide range of possible diseases and their effects on the plant and the *** study presents an adaptive histogram-based method for solving this *** method describe is domain independent in the sense that it can be easily and efficiently adapted to other similar smart agriculture *** approach explores colour spaces,namely,Red,Green,and Blue along with *** histograms of colour spaces and grey space are analysed based on the notion that as the disease changes,the colour also *** proximity between the histograms of grey images with individual colour spaces is estimated to find the closeness of *** the grey image is the average of colour spaces(R,G,and B),it can be considered a reference *** estimating the distance between grey and colour spaces,the proposed approach uses a Chi-Square distance ***,the method uses an Artificial Neural Network for *** effectiveness of our approach is demonstrated by testing on a dataset of fruit and leaf images affected by different *** results show that the method outperforms existing techniques in terms of average classification rate.

关键词： color spaces distance measure fruit classification leaf classification plant disease classification

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Navigating Anxiety in a Cloud E-learning Virtual Environments: The Moderating Role of Anxiety on Lecturers’ Adoption of Cloud Computing E-learning

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第17期18卷 115-138页

作者： Armah, Elisha D’Archimedes Sathik Ali, I. Department of Computer Applications B.S. Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu Chennai India Department of Computer Science and Technology Cape Coast Technical University Cape Coast Ghana Department of Information Technology B.S. Abdur Rahman Crescent Institute of Science and Technology Tamil Nadu Chennai India

The rapid expansion of cloud computing e-learning systems has empowered educators to reach learners worldwide. However, a critical study gap exists: the impact of lecturers’ technological anxiety on their adoption intentions remains unexplored in the technical universities in Ghana. This study addresses this gap by investigating anxiety’s influence on lecturers’ cloud computing e-learning adoption intentions within a virtual environment (VE). We examine the mediating role of competitive pressure and the moderating role of anxiety. Using PLSSEM, we analysed data from surveys conducted among 1395 lecturers across all 10 technical universities. With a high response rate of 90.2% (n = 1258), our analysis reveals moderate anxiety among lecturers regarding cloud e-learning adoption. The proposed model explains 21.1% of the variance in adoption intentions. Interestingly, competitive pressure on lecturers mediates the relationship between cloud e-learning usefulness, security, and adoption decisions. Additionally, anxiety moderates the effects of cloud computing complexity, security, and adoption intentions. We have recommended organizing regular training workshops, ensuring user-friendly platforms, implementing robust security measures, and providing psychological support for lecturers using cloud computing technologies. © 2024 by the authors of this article.

关键词： Virtual environments

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Prompt-augmented Feature with Cross-domain Contrastive Learning for Efficient Multi-domain Sentiment Analysis

Prompt-augmented Feature with Cross-domain Contrastive Learn...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Li, Rui Yang, Tao Liu, Cheng Jiang, Dazhi Wong, Hau-San Wu, Si Department of Computer Science Shantou University China Department of Computer Science City University of Hong Kong Hong Kong School of Computer Science and Engineering South China University of Technology China

ISBN: (纸本)9798350368741

Pre-trained language models (PrLMs) demonstrate impressive performance on the sentiment analysis task. However, the large number of trainable parameters brings about heavy computational costs, which become more serious in multi-domain scenarios. In this paper, we propose to extract multi-layer features from the PrLM for efficient training since the training process is independent to its large backbone. Meanwhile, compared with the conventional feature extraction, we leverage prompts to induce PrLM for generating sentiment-aware features which lead to significant improvement on the sentiment analysis. In addition, most previous methods adopted a domain alignment paradigm for multi-domain learning, which becomes cumbersome when the number of domains is large. Therefore, we propose a novel prompt-augmented cross-domain contrastive learning for generalizable performance, which clusters samples with the same label under different prompts or domains. Our method is evaluated on two public multi-domain sentiment analysis benchmarks, which significantly outperforms recent state-of-the-art methods. Extensive ablation studies also verify the effectiveness of each proposed component. © 2025 IEEE.

关键词： multi-domain learning pre-trained language model prompt augmentation Sentiment analysis sentiment-aware feature

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Innovative Approaches to Task Scheduling in Cloud Computing Environments Using an Advanced Willow Catkin Optimization Algorithm

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computers, Materials & Continua 2025年第2期82卷 2495-2520页

作者： Jeng-Shyang Pan Na Yu Shu-Chuan Chu An-Ning Zhang Bin Yan Junzo Watada College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China Department of Information Management Chaoyang University of TechnologyTaichung41349Taiwan College of Electronics Communication and PhysicsShandong University of Science and TechnologyQingdao266590China Graduate School of Information Production and SystemsWaseda UniversityKitakyushu808-0135Japan

The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resources for optimized resource utilization. Several meta-heuristic algorithms have shown effectiveness in task scheduling, among which the relatively recent Willow Catkin Optimization (WCO) algorithm has demonstrated potential, albeit with apparent needs for enhanced global search capability and convergence speed. To address these limitations of WCO in cloud computing task scheduling, this paper introduces an improved version termed the Advanced Willow Catkin Optimization (AWCO) algorithm. AWCO enhances the algorithm’s performance by augmenting its global search capability through a quasi-opposition-based learning strategy and accelerating its convergence speed via sinusoidal mapping. A comprehensive evaluation utilizing the CEC2014 benchmark suite, comprising 30 test functions, demonstrates that AWCO achieves superior optimization outcomes, surpassing conventional WCO and a range of established meta-heuristics. The proposed algorithm also considers trade-offs among the cost, makespan, and load balancing objectives. Experimental results of AWCO are compared with those obtained using the other meta-heuristics, illustrating that the proposed algorithm provides superior performance in task scheduling. The method offers a robust foundation for enhancing the utilization of cloud computing resources in the domain of task scheduling within a cloud computing environment.

关键词： Willow catkin optimization algorithm cloud computing task scheduling opposition-based learning strategy

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A Benchmark Dataset for Cricket Sentiment Analysis in Bangla Social Media Text 15

A Benchmark Dataset for Cricket Sentiment Analysis in Bangla...

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15th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2024 / The 7th International Conference on Emerging Data and Industry 4.0, EDI40 2024

作者： Mahmud, Tanjim Karim, Rezaul Chakma, Rishita Chowdhury, Tanjia Hossain, Mohammad Shahadat Andersson, Karl Department of Computer Science and Engineering Rangamati Science and Technology University Bangladesh Department of Computer Science and Engineering Southern University Bangladesh Chittagong Bangladesh Department of Computer Science and Engineering University of Chittagong Chittagong Bangladesh Lulea University of Technology Skelleftea Sweden

This study introduces a novel benchmark dataset designed for Cricket Sentiment Analysis on Bangla social media posts, emphasizing a low-resource setting. The dataset was meticulously curated through manual collection across diverse social media platforms, ensuring comprehensive representation of user sentiments. Annotations validated dataset quality, achieving a remarkable Cohen Kappa score of 0.97. Experimentation with machine learning (ML) models revealed challenges, with traditional approaches yielding modest RNN accuracy of 0.5239. However, deep learning (DL) models showcased significant performance enhancements. The LSTM model achieved 0.897 accuracy, while the BiLSTM model surpassed expectations at 0.952. These findings highlight DL's efficacy in capturing nuanced sentiments in Bangla cricket-related social media posts, contributing a high-quality benchmark dataset and insights into DL's suitability for sentiment analysis in low-resource linguistic contexts. © 2024 Elsevier B.V.. All rights reserved.

关键词： Sentiment analysis

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A Comparison of Machine Learning Techniques for Yemeni Universities Admission Examinations Predictions 1

A Comparison of Machine Learning Techniques for Yemeni Unive...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Al-Hagree, Salah Al-Sanabani, Maher Al-Gaphari, Ghaleb Al-Shalabi, Ahmed Abdullah Alawdi, Ala'a Al-Dilami, Redwan Mohsen, Ayedh Abdulaziz Alfahad, Khalil T. Computer Science Department Faculty of Computer and Information Technology Sana’a University Yemen Computer Science Department Faculty of Sciences Ibb University Yemen Aljazeera University Yemen Computer Science Department Faculty of Computer and Information Technology Thamar University Yemen Faculty of Computing and IT University of Science and Technology Sana’a Yemen School of Mathematics and Computer Science Wuhan Textile University China

ISBN: (纸本)9798331533557

Admissions examinations are crucial in shaping students' academic and career paths. Machine learning (ML) offers a promising avenue for transforming the assessment process in universities, schools, and colleges. This paper presents a new ML-based method to evaluate high school graduates seeking admission to eleven colleges across nine Yemeni universities, applicable to both scholarship and self-financing students. The proposed approach leverages several ML techniques, including Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), K-Nearest Neighbors (KNN), Gradient Boosting (GB), Extra Trees (ET), Decision Tree (DT), AdaBoost, and XGBoost. In this study, ML algorithms were applied to admissions examinations at nine Yemeni universities, including Sana’a, Ibb, Dhamar, Amran, Jiblah, Mihwit, Al-Dhalea, Saada, and Hajjah. The dataset comprised 15,898 student applications for eleven colleges, encompassing both scholarship and self-financing programs. Experiment one evaluated the performance of various ML techniques. Gradient Boosting (GB) and Random Forest (RF) demonstrated superior results, achieving the highest accuracy, precision, recall, and F-score. Specifically, GB and RF attained 91.00% and 90.00% accuracy, 87.60% and 88.80% precision, 89.90% and 87.50% recall, and 89.30% and 87.60% F-score, respectively. Experiment two further refined the analysis, comparing ET and RF to other ML methods. ET and RF consistently outperformed LR, KNN, SVM, DT, GB, AdaBoost, and XGBoost. In this experiment, ET and RF achieved exceptional accuracy, precision, recall, and F-score rates of 99.00% and 99.00%, 97.50% and 97.20%, 98.00% and 97.80%, and 97.80% and 97.50%, respectively. © 2024 IEEE.

关键词： Random forests

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Integrating Low-order and High-order Functional Connectivity for Meta-stable State Transition based Brain Disorder Identification

Integrating Low-order and High-order Functional Connectivity...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Pei, Shengbing Wang, Yan He, Fan Lv, Zhao Zhang, Chao Guan, Jihong Anhui University School of Computer Science and Technology Anhui China Tongji University Department of Computer Science and Technology Shanghai China

ISBN: (纸本)9798350386226

Dynamic functional connectivity network (FCN) can effectively mine meta-stable state transition within the period of data acquisition time, which is related to neurological diseases. However, conventional FCN directly describes the correlation between two brain regions in a meta-stable state, it is low-order FCN. In fact, the connection between two brain regions within the meta-stable state also has a changing pattern, which can reveal the functional consistency between two connections over time, we denote the changing pattern between two regions as high-order FCN. Here, we propose an end-to-end method that integrates low-order and high-order dynamic FCNs for better brain disorder identification. First, a sliding window operation is adopted to capture meta-stable states. Then, a matrix variate normal distribution based approach is employed to construct the low-order and high-order FCNs for each meta-stable state. Finally, a two-stage Transformer is designed to extract meta-stable state transition feature for classification. Experimental results on the ADHD-200 and ABIDE datasets indicate that: 1) our proposed method integrate multi-level connectivity information of dynamic brain functions, thereby effectively improving the identification of brain disorders;2) the proposed two-stage Transformer is more effective in feature extraction than the well-known CNN-LSTM architecture;3) high-order FCN help locate biomarkers that low-order FCN cannot be determine, including brain regions as well as functional connections between brain regions, which contribute significantly to the diagnosis of brain disorders. © 2024 IEEE.

关键词： Magnetic resonance imaging

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