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Provably Secure Efficient Key-Exchange Protocol for Intelligent Supply Line Surveillance in Smart Grids

IEEE Internet of Things Journal 2025年第12期12卷 21784-21792页

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Alenazi, Mohammed J.F. Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan 250353 China

Intelligent supply line surveillance is critical for modern smart grids. Smart sensors and gateway nodes are strategically deployed along supply lines to achieve intelligent surveillance. They collect data continuously and transmit it to the control centre in real-time. It enables real-time monitoring, fault detection, and efficient energy management across distribution networks. This advanced surveillance system ensures continuous monitoring of supply lines, detecting anomalies, and optimizing operations to maintain the stability and reliability of the smart grid. However, the reliance of all participating nodes on public communication channels to transmit supply line surveillance data exposes these systems to critical cyber attacks. These cyber attacks include impersonation, physical tampering, ephemeral secret leakage, and desynchronization attacks. To address these issues, existing key-exchange protocols often fail to ensure robust security while imposing high computation and communication overheads, limiting their practicality for resource-constrained environments. Therefore, we propose a secure and efficient key exchange and tamper-resistant authentication protocol using Elliptic Curve Cryptography (ECC) and Physical Unclonable Functions (PUFs). The PUF mechanism provides robust resistance against physical tampering and cloning attacks, ensuring enhanced physical security for supply line devices. We validate the security robustness of the devised protocol using the Random or Real (ROR) model. Furthermore, informal security analysis demonstrates the protocol's robustness in rigorously resisting various attacks, including physical tampering, impersonation, ephemeral secret leakage and desynchronization. Moreover, we determine the performance evaluation that reveals the protocol's superior efficiency compared to competing protocols, achieving significant reductions of 28.64% in computation overhead and 9.96% in communication overhead. © 2014 IEEE.

关键词： Authentication Protocol

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Weather Forecasting Prediction Using Ensemble Machine Learning for Big Data Applications

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computers, Materials & Continua 2022年第11期73卷 3367-3382页

作者： Hadil Shaiba Radwa Marzouk Mohamed K Nour Noha Negm Anwer Mustafa Hilal Abdullah Mohamed Abdelwahed Motwakel Ishfaq Yaseen Abu Sarwar Zamani Mohammed Rizwanullah Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Faculty of Science Mathematics and Computer Science DepartmentMenoufia UniversityEgypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Research Centre Future University in EgyptNew Cairo11845Egypt

The agricultural sector’s day-to-day operations,such as irrigation and sowing,are impacted by the ***,weather constitutes a key role in all regular human *** forecasting must be accurate and precise to plan our activities and safeguard ourselves as well as our property from ***,wind speed,humidity,wind direction,cloud,temperature,and other weather forecasting variables are used in this work for weather *** research works have been conducted on weather *** drawbacks of existing approaches are that they are less effective,inaccurate,and *** overcome these issues,this paper proposes an enhanced and reliable weather forecasting *** well as developing weather forecasting in remote *** data analysis and machine learning techniques,such as Gradient Boosting Decision Tree,Random Forest,Naive Bayes Bernoulli,and KNN Algorithm are deployed to anticipate weather conditions.A comparative analysis of result outcome said in determining the number of ensemble methods that may be utilized to improve the accuracy of prediction in weather *** aim of this study is to demonstrate its ability to predict weather forecasts as soon as *** evaluation shows our ensemble technique achieves 95%prediction ***,for 1000 nodes it is less than 10 s for prediction,and for 5000 nodes it takes less than 40 s for prediction.

关键词： Weather forecasting KNN random forest gradient boosting decision tree naive bayes bernoulli

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The Local Resolving Dominating Set of Comb Product Graphs

IAENG International Journal of Computer Science

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IAENG International Journal of computer science 2024年第2期51卷 115-120页

作者： Umilasari, Reni Susilowati, Liliek Slamin Prabhu, Savari Fadekemi, Osaye J. Universitas Muhammadiyah Jember Indonesia Mathematics Department Airlangga University Indonesia Department of Informatics System Jember University Indonesia Mathematics Department Rajalakshmi Engineering College Tamil Nadu Chennai602105 India Department of Mathematics and Computer Science Alabama State University United States

The local resolving dominating set studied in this paper is a notion that combines two concepts in graph theory, the local metric dimension and dominations in graphs. Let G and H be connected graphs of orders n and m, respectively;and x a vertex in H hereafter referred to as a linkage vertex. The comb product of G and H denoted by G.H, is a graph obtained by taking one copy of G and n copies of H and attaching the i-th copy of H at the vertex x to the i-th vertex of G. In this paper, we determine the local resolving dominating set of the comb products G. Sn with two different linkage vertices, G. Kn, G. Km,n and G. Cm graphs. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Graph theory

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Data science based Recommendation system - An Application of computer science 14

Data Science based Recommendation System - An Application of...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Khan, Zeba Rahman, Abdul Applied College Jazan University Department of Computer and Information System Jazan Saudi Arabia Presidency University School of Computer Science and Engineering & Information Science Rajanukunte Yelahanka Bangalore India

Recommender system (RS) has emanated as the most popular application of e-commerce websites. In the e-commerce business, collaborative filtering based RS systems suggest products to the customers and find the interesting things for the users which they may wish to purchase. The success rate of any recommendation system depends upon, information reliability as well as expression of daily life behavior in the consolidated format. The main task is to produce the best ranked list of ‘n-number’ of items for the user’s need. Due to the natural structure, Z-Numbers are more consistent to producing a recommendation list. To solve real life problem, Z-Numbers should be incorporated into decision-making models. However, Online shopping involved multicriteria group decision-making (MCGDM). Z-information persisting some difficulties with MCGDM. Therefore, to enhancing the ability of Z-numbers Complex Fuzzy Sets (CFSs), are employed. Besides this entropy, distance measure, and aggregation operator are combined to produced MCGDM based ranking of customer preference. © Grenze Scientific Society, 2023.

关键词： Recommender systems

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Arabic Sign Language Gesture Classification Using Deer Hunting Optimization with Machine Learning Model

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computers, Materials & Continua 2023年第5期75卷 3413-3429页

作者： Badriyya B.Al-onazi Mohamed K.Nour Hussain Alshahran Mohamed Ahmed Elfaki Mrim M.Alnfiai Radwa Marzouk Mahmoud Othman Mahir M.Sharif Abdelwahed Motwakel Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science Faculty of Computer Science and Information TechnologyOmdurman Islamic UniversityOmdurman14415Sudan Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Sign language includes the motion of the arms and hands to communicate with people with hearing *** models have been available in the literature for sign language detection and classification for enhanced *** the latest advancements in computer vision enable us to perform signs/gesture recognition using deep neural *** paper introduces an Arabic Sign Language Gesture Classification using Deer Hunting Optimization with Machine Learning(ASLGC-DHOML)*** presented ASLGC-DHOML technique mainly concentrates on recognising and classifying sign language *** presented ASLGC-DHOML model primarily pre-processes the input gesture images and generates feature vectors using the densely connected network(DenseNet169)*** gesture recognition and classification,a multilayer perceptron(MLP)classifier is exploited to recognize and classify the existence of sign language ***,the DHO algorithm is utilized for parameter optimization of the MLP *** experimental results of the ASLGC-DHOML model are tested and the outcomes are inspected under distinct *** comparison analysis highlighted that the ASLGC-DHOML method has resulted in enhanced gesture classification results than other techniques with maximum accuracy of 92.88%.

关键词： Machine learning sign language recognition multilayer perceptron deer hunting optimization densenet

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Agile Factor Flexible Industry 4.0 with MES Manufacturing Execution system Along with ERP Back-End Integration: Ready-Made Garments as a Case Study

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American Journal of Operations Research 2024年第4期14卷 137-150页

作者： Mohamed Mostafa Mohamed Abdulaziz Saleh Alraddadi AppSoft Co. Alexandria Egypt Department of Networks and Information System College of Computer Science and Engineering Taibah University Yanbu Saudi Arabia

Industry 4.0, or the Fourth Industrial Revolution, is based on digitized the manufacturing process and makes use of all digital tools so its combination of various digital technologies computers, ERP software, IoT, machine learning and AI techniques, Manufacturing Execution systems (MES), and big data analytics to create a new, fully digitized manufacturing system. The Critical Success Factors (CSFs) of MES adoption are both a quantitative and qualitative measurement. We use the case of ready-made garments to improve each of the three Overall Equipment Efficiency (OEE) factors: Availability, Performance, and Quality. In this study, we adopt real-time management of production activities on the shop floor from order receipt to finished products, then measure the improvement.

关键词： Industry 4.0 ERP MES IOT

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Enhancing Image Security and Privacy Using Double Random Phase Encoding Based Chaotic system and Compressed Sensing

Enhancing Image Security and Privacy Using Double Random Pha...

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Jordanian Cybersecurity Conference (IJCC), International

作者： Khalid Thaer Alnidawi Ali Makki Sagheer Department of Computer Science College of Computer Science & IT University of Anbar Ramadi Iraq Department of Computer Networks Systems College of Computer Science & IT University of Anbar Ramadi Iraq

ISBN: (数字)9798331518462

ISBN: (纸本)9798331518479

With the rapid advancements in multimedia communications, safeguarding image data holds significant aspect of the cyber security field. However, it is necessary to ensure the security of transmitted data and maintain the privacy issue. The Optical Imaging Encryption techniques have attracted serious interest due to its efficiency of concealing information in multiple dimensions by encoding the original signal in the spatial and frequency domains and the efficient parallel processing of two-dimensional images. In addition, Chaos-based encryption provides a powerful defense against cryptographic attacks due to its properties, for instance, nonlinearity and sensitivity to initial conditions. To exploit the inherent unpredictability and complexity of chaotic system by combining the benefits of chaotic maps and Double Random Phase Encoding (DRPE), a robust DRPE technique based on chaotic maps and compressed sensing is proposed. Where, a two random phase masks (RPMs) are generated using chaotic JSMP map to eliminate the need of key transmission in secure channels, to provide high unpredictability and key sensitivity, to strengthen the encryption system. Secondly, a DCT transform is used to decompose the image into its frequency coefficients. Then, the frequency coefficients are minimized by using compressed sensing technique to compress the image data and enhancing storage management and transmission time. Finally, the output matrix will be used as an input to the (DRPE) system to generate the cipher image. The combination of these techniques ensures reliability against cyber threats such as differential cryptanalysis and other attacks.

关键词： Data privacy Visualization Image coding Sensitivity Chaotic communication Storage management Encryption Discrete cosine transforms Compressed sensing Image reconstruction

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Automated Multi-Document Biomedical Text Summarization Using Deep Learning Model

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computers, Materials & Continua 2022年第6期71卷 5799-5815页

作者： Ahmed S.Almasoud Siwar Ben Haj Hassine Fahd N.Al-Wesabi Mohamed K.Nour Anwer Mustafa Hilal Mesfer Al Duhayyim Manar Ahmed Hamza Abdelwahed Motwakel Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversitySaudi Arabia Department of Computer Science College of Science and Arts at MahayilKing Khalid UniversitySaudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Due to the advanced developments of the Internet and information technologies,a massive quantity of electronic data in the biomedical sector has been exponentially *** handle the huge amount of biomedical data,automated multi-document biomedical text summarization becomes an effective and robust approach of accessing the increased amount of technical and medical literature in the biomedical sector through the summarization of multiple source documents by retaining the significantly informative ***,multi-document biomedical text summarization acts as a vital role to alleviate the issue of accessing precise and updated *** paper presents a Deep Learning based Attention Long Short Term Memory(DLALSTM)Model for Multi-document Biomedical Text *** proposed DL-ALSTM model initially performs data preprocessing to convert the available medical data into a compatible format for further ***,the DL-ALSTM model gets executed to summarize the contents from the multiple biomedical *** order to tune the summarization performance of the DL-ALSTM model,chaotic glowworm swarm optimization(CGSO)algorithm is *** experimentation analysis is performed to ensure the betterment of the DL-ALSTM model and the results are investigated using the PubMed *** comparative result analysis is carried out to showcase the efficiency of the proposed DL-ALSTM model with the recently presented models.

关键词： Biomedical text summarization healthcare deep learning lstm parameter tuning

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Congestion Control Mechanisms and Techniques in computer Network: A Review

Congestion Control Mechanisms and Techniques in Computer Net...

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2022 International Conference on Data science and Intelligent Computing, ICDSIC 2022

作者： Altameemi, Ahmed Malik Nassar, Khulood A. College of Computer Science and Information Technology University of Basrah Department of Computer Information System Basrah Iraq

ISBN: (纸本)9798350334883

One of the most important and most pioneering topics in the field of networks is congestion and how to control it or avoid its occurrence because this topic has a huge impact on the network and the quality of service (QoS). Congestion reduces the efficiency of the network and stops the service in many cases. Therefore, there is an urgent need to develop techniques and mechanisms to avoid congestion or reduce its impact to the extent possible to maintain the permanence of data flow in the network. Congestion is avoided at two levels, node and link, by using different techniques, including closed loop or open loop. This paper reviews an explanation of congestion, how it occurs, and its impact on the network, and then shows the mechanisms through which congestion eliminated can be bed or avoided before it occurs. It also reviews a set of algorithms previously used by researchers in various types of networks and compares the performance between them. © 2022 IEEE.

关键词： Quality of service

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A Comparative Study on Deep Learning Models for Stock Price Prediction 5

A Comparative Study on Deep Learning Models for Stock Price ...

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5th Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2024

作者： He, Zhexin Zhang, Huan Por, Lip Yee Chongqing College of Architecture and Technology Chongqing401331 China Chongqing Academy of Science and Technology Chongqing401123 China Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia

ISBN: (纸本)9781643685243

With the advancement of deep learning technology, researchers have begun employing deep neural network models such as LSTM, MLP, CNN, and GRU to tackle nonlinear prediction problems in stock markets. This study harnesses these deep learning models, including LSTM, MLP, CNN, and GRU, to investigate the nonlinear forecasting of stock market price indices. The experiment selects globally representative stock indices and, through analyzing short-term, mid-term, and long-term data, discovers that the GRU model demonstrates exceptional performance in both prediction accuracy and generalization capabilities. To further enhance precision, the research constructs a filtering ensemble decision-making system based on multiple GRU models. This strategy reduces the error by at least 0.3% in medium to long-term predictions, significantly outperforming a single standalone GRU model. Consequently, the GRU-filtered ensemble decision model has been empirically proven as an effective tool for predicting stock indices, not only broadening the methods available for stock market prediction but also deepening the understanding and application of the generalization abilities of deep learning models within financial markets. © 2024 The Authors.

关键词： Long short-term memory

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