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An Efficient Technique to Prevent Data Misuse with Matrix Cipher Encryption Algorithms

computers, Materials & Continua 2023年第2期74卷 4059-4079页

作者： Muhammad Nadeem Ali Arshad Saman Riaz Syeda Wajiha Zahra Ashit Kumar Dutta Moteeb Al Moteri Sultan Almotairi Department of Computing Abasyn UniversityIslamabadPakistan Department of Computer Science National University of TechnologyIslamabadPakistan Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Kingdom of Saudi Arabia Department of Management Information Systems Business Administration CollegeKing Saud UniversityRiyadh11451Saudi Arabia Department of Natural and Applied Sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia

Many symmetric and asymmetric encryption algorithms have been developed in cloud computing to transmit data in a secure *** cryptography is a data encryption mechanism that consists of different steps and prevents the attacker from misusing the *** paper has developed an efficient algorithm to protect the data from invaders and secure the data from *** this algorithm is applied to the cloud network,the attacker will not be able to access the *** encrypt the data,the values of the bytes have been obtained by converting the plain text to ASCII.A key has been generated using the Non-Deterministic Bit Generator(NRBG)mechanism,and the key is XNORed with plain text bits,and then Bit toggling has been *** that,an efficient matrix cipher encryption algorithm has been developed,and this algorithm has been applied to this *** capability of this algorithm is that with its help,a key has been obtained from the plain text,and only by using this key can the data be decrypted in the first steps.A plain text key will never be used for another plain *** data has been secured by implementing different mechanisms in both stages,and after that,a ciphertext has been *** the end of the article,the latest technique will be compared with different *** will be a discussion on how the present technique is better than all the other techniques;then,the conclusion will be drawn based on comparative analysis.

关键词： Symmetric cryptography ciphertext encryption decryption cloud security matrix cipher

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WanderGenie: A Smart Guide for Immersive Tourism through Generative AI 2

WanderGenie: A Smart Guide for Immersive Tourism through Gen...

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2nd International Conference on Computational Intelligence and Network systems, CINS 2024

作者： Barvin, D.S. Rajan, Anitha Amaithi Rajan, Arun Amaithi Department of Computer Science Business Systems Francis Xavier Engineering College Tamil Nadu Tirunelveli627011 India Department of Computer Science and Engineering College of Engineering Guindy Anna University Tamil Nadu Chennai600025 India

ISBN: (纸本)9798331504106

Tourism encourages cultural interaction, economic growth, and global connectivity, improving both travelers and host communities. Due to the shortage of accessible and competent guides, travelers often miss out on the rich history and cultural value of the areas they visit, leading to a shallow understanding of these sites. To bridge this knowledge gap, this article proposes WanderGenie: A Smart Guide for Immersive Tourism through Generative AI to enhance the visitor experience at popular places where traditional guides are unavailable. The system leverages image processing and RFID technology to recognize landmarks and provide travelers with real-time as well as detailed information about the spots they visit. The system ensures accurate landmark identification by strategically inserting RFID tags at precise spots. Upon detecting a landmark, the system shows relevant content through visual displays and audio outputs, creating an immersive and participatory experience. Acting as a kind virtual assistant, WanderGenie answers tourist’s queries, improving their understanding and appreciation of the attractions they experience by utilizing Generative AI technology. This concept solves the shortage of guides while raising the quality of the tourist experience by making historical, cultural, and architectural knowledge more accessible. The proposed system has been designed, implemented, and validated successfully. ©2024 IEEE.

关键词： Tourism

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IoT-Cloud Assisted Botnet Detection Using Rat Swarm Optimizer with Deep Learning

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computers, Materials & Continua 2023年第2期74卷 3085-3100页

作者： Saeed Masoud Alshahrani Fatma S.Alrayes Hamed Alqahtani Jaber S.Alzahrani Mohammed Maray Sana Alazwari Mohamed A.Shamseldin Mesfer Al Duhayyim Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computer ScienceCenter of Artificial IntelligenceUnit of CybersecurityKing Khalid UniversityAbhaSaudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif P.O.Box 11099Taif21944Saudi Arabia Department of Mechanical Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia

Nowadays,Internet of Things(IoT)has penetrated all facets of human life while on the other hand,IoT devices are heavily prone to *** has become important to develop an accurate system that can detect malicious attacks on IoT environments in order to mitigate security *** is one of the dreadfulmalicious entities that has affected many users for the past few *** is challenging to recognize Botnet since it has excellent carrying and hidden *** approaches have been employed to identify the source of Botnet at earlier *** Learning(ML)and Deep Learning(DL)techniques are developed based on heavy influence from Botnet detection *** spite of this,it is still a challenging task to detect Botnet at early stages due to low number of features accessible from Botnet *** current study devises IoT with Cloud Assisted Botnet Detection and Classification utilizingRat SwarmOptimizer with Deep Learning(BDC-RSODL)*** presented BDC-RSODL model includes a series of processes like pre-processing,feature subset selection,classification,and parameter ***,the network data is pre-processed to make it compatible for further ***,RSO algorithm is exploited for effective selection of subset of ***,Long Short TermMemory(LSTM)algorithm is utilized for both identification and classification of ***,Sine Cosine Algorithm(SCA)is executed for fine-tuning the hyperparameters related to LSTM *** order to validate the promising 3086 CMC,2023,vol.74,no.2 performance of BDC-RSODL system,a comprehensive comparison analysis was *** obtained results confirmed the supremacy of BDCRSODL model over recent approaches.

关键词： Internet of things cloud computing long short termmemory deep learning sine cosine algorithm feature selection

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When Computing Power Network Meets Distributed Machine Learning: An Efficient Federated Split Learning Framework 31

When Computing Power Network Meets Distributed Machine Learn...

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31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Yuan, Xinjing Pu, Lingjun Jiao, Lei Wang, Xiaofei Yang, Meijuan Xu, Jingdong Institute of Systems and Networks College of Computer Science Nankai University China University of Oregon Department of Computer Science United States School of Computer Science and Technology Tianjin University China

ISBN: (纸本)9798350399738

In this paper, we advocate CPN-FedSL, a novel and flexible Federated Split Learning (FedSL) framework over Computing Power Network (CPN). We build a dedicated model to capture the basic settings and learning characteristics (e.g., training flow, latency and convergence). Based on this model, we introduce Resource Usage Effectiveness (RUE), a novel performance metric integrating training utility with system cost, and formulate a multivariate scheduling problem that maximizes RUE by comprehensively taking client admission, model partition, server selection, routing and bandwidth allocation into account (i.e., mixed-integer fractional programming). We design Refinery, an efficient approach that first linearizes the fractional objective and non-convex constraints, and then solves the transformed problem via a greedy based rounding algorithm in multiple iterations. Extensive evaluations corroborate that CPN-FedSL is superior to the standard and state-of-the-art learning frameworks (e.g., FedAvg and SplitFed), and besides Refinery is lightweight and significantly outperforms its variants and de facto heuristic methods under a variety of settings. © 2023 IEEE.

关键词： Integer programming

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A Three-Dimensional Real-Time Gait-Based Age Detection System Using Machine Learning

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computers, Materials & Continua 2023年第4期75卷 165-182页

作者： Muhammad Azhar Sehat Ullah Khalil Ullah Habib Shah Abdallah Namoun Khaliq Ur Rahman Department of Computer Science&IT University of MalakandChakdara18800Pakistan Department of Software Engineering University of MalakandChakdara18800Pakistan College of Computer Science King Khalid UniversityAbha62529Saudi Arabia Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Statistics Abdul Wali Khan UniversityMardan23200Pakistan

Human biometric analysis has gotten much attention due to itswidespread use in different research areas, such as security, surveillance,health, human identification, and classification. Human gait is one of the keyhuman traits that can identify and classify humans based on their age, gender,and ethnicity. Different approaches have been proposed for the estimation ofhuman age based on gait so far. However, challenges are there, for which anefficient, low-cost technique or algorithm is needed. In this paper, we proposea three-dimensional real-time gait-based age detection system using a machinelearning approach. The proposed system consists of training and testingphases. The proposed training phase consists of gait features extraction usingthe Microsoft Kinect (MS Kinect) controller, dataset generation based onjoints’ position, pre-processing of gait features, feature selection by calculatingthe Standard error and Standard deviation of the arithmetic mean and bestmodel selection using R2 and adjusted R2 techniques. T-test and ANOVAtechniques show that nine joints (right shoulder, right elbow, right hand, leftknee, right knee, right ankle, left ankle, left, and right foot) are statisticallysignificant at a 5% level of significance for age estimation. The proposedtesting phase correctly predicts the age of a walking person using the resultsobtained from the training phase. The proposed approach is evaluated on thedata that is experimentally recorded from the user in a real-time *** (50) volunteers of different ages participated in the experimental *** the limited features, the proposed method estimates the age with 98.0%accuracy on experimental images acquired in real-time via a classical generallinear regression model.

关键词： Age estimation gait biometrics classical linear regression model

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Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution

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computers, Materials & Continua 2023年第2期74卷 2379-2395页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Sameer Alshetewi Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt General Information Technology Department Ministry of DefenseThe Executive AffairsExcellence Services DirectorateRiyadh11564Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Electrocardiogram(ECG)signal is a measure of the heart’s electrical ***,ECG detection and classification have benefited from the use of computer-aided systems by *** goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization(DTO)and Differential Evolution Algorithm(DEA)into a unified algorithm to optimize the hyperparameters of neural network(NN)for boosting the ECG classification *** addition,we proposed a new feature selection method for selecting the significant feature that can improve the overall *** prove the superiority of the proposed approach,several experimentswere conducted to compare the results achieved by the proposed approach and other competing ***,statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA *** results confirmed the superiority and effectiveness of the proposed *** classification accuracy achieved by the proposed approach is(99.98%).

关键词： Electrocardiogram differential evolution algorithm dipper throated optimization neural networks

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Forecasting Price Prediction for Vegetables and Fruits Using Recurrent Neural Network 5

Forecasting Price Prediction for Vegetables and Fruits Using...

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5th International Conference on Electronics and Sustainable Communication systems, ICESC 2024

作者： Gothai, E. Rajalaxmi, R.R. Thamilselvan, R. Harshath, S.M. Kongu Engineering College Department of Computer Technology Tamilnadu Perundurai India Kongu Engineering College Department of Computer Science and Engineering Tamilnadu Perundurai India Kongu Engineering College MSc Software Systems Erode India

ISBN: (纸本)9798350379945

Unpredictable fruit and vegetable prices create significant challenges for farmer livelihoods. This research proposes an innovative approach using recurrent neural networks (RNNs) to predict both minimum and maximum prices. By analyzing historical market data, these models can forecast potential price ranges, empowering farmers to make informed decisions about planting, harvesting, and sales strategies. This enhanced foresight helps farmers navigate market volatility and improve income stability. © 2024 IEEE.

关键词： Vegetables

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Enhancing Cybersecurity in IIoT Smart Grids: Leveraging HGAMPO for PMU Data Manipulation Detection

Enhancing Cybersecurity in IIoT Smart Grids: Leveraging HGAM...

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2024 International Conference on Knowledge Engineering and Communication systems, ICKECS 2024

作者： Janani, B. Keerthana, S.D. Subathra, S. Subiksha, N. K. S. Rangasamy College of Technology Department of Computer Science and Engineering Tiruchengode637 215 India Sri Sai Ram Engineering College Department of Computer Science and Business Systems Chennai600044 India

ISBN: (纸本)9798350359688

The integration of Industrial Internet of Things (IIoT) technology into smart grid systems has resulted in unprecedented advantages in terms of effectiveness and dependability. Nevertheless, this interconnectivity also brings about fresh cybersecurity obstacles, specifically with the identification and prevention of Phasor Measurement Unit (PMU) data manipulation threats. Efficient feature selection is vital in improving the robustness of IIoT smart grids against such attacks. The Hybrid Green Anaconda and Marine Predictor Optimization (HGAMPO) algorithm is a promising strategy for choosing significant features in the detection of manipulation in PMU data. This paper presents a comprehensive examination of PMU data manipulation attacks, emphasises the significance of feature selection, and introduces the HGAMPO algorithm. HGAMPO is a revolutionary approach that combines the Green Anaconda Optimization Algorithm (GAOA) and Marine Predictor Optimization (MPO) to optimise feature subsets. It draws inspiration from nature to provide an innovative solution. The effectiveness of HGAMPO in IIoT smart grid contexts is proved through real-world examples and compared with older approaches. The evaluation metrics and experimental results clearly demonstrate the exceptional performance of HGAMPO in detecting attacks that manipulate PMU data. This article provides an overview of upcoming study areas and the possible influence of HGAMPO on cybersecurity in IIoT smart grids. © 2024 IEEE.

关键词： Smart power grids

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Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

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computers, Materials & Continua 2023年第2期74卷 4531-4545页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Mostafa Abotaleb Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine *** feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical *** a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this *** can result when the selection of features is subject to metaheuristics,which can lead to a wide range of ***,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more *** propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of *** the proposed method,the number of features selected is minimized,while classification accuracy is *** test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.

关键词： Metaheuristics dipper throated optimization grey wolf optimization binary optimizer feature selection

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An Intelligent Secure Adversarial Examples Detection Scheme in Heterogeneous Complex Environments

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computers, Materials & Continua 2023年第9期76卷 3859-3876页

作者： Weizheng Wang Xiangqi Wang Xianmin Pan Xingxing Gong Jian Liang Pradip Kumar Sharma Osama Alfarraj Wael Said College of Information Science and Engineering HunanWomen’s UniversityChangsha410138China School of Mathematics and Statistics Hunan First Normal UniversityChangsha410138China School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114China Department of Computing Science University of AberdeenAberdeenAB243FXUK Department of Computer Science Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computer Science Faculty of Computers and InformaticsZagazig UniversityZagazig44511Egypt

Image-denoising techniques are widely used to defend against Adversarial Examples(AEs).However,denoising alone cannot completely eliminate adversarial *** remaining perturbations tend to amplify as they propagate through deeper layers of the network,leading to ***,image denoising compromises the classification accuracy of original *** address these challenges in AE defense through image denoising,this paper proposes a novel AE detection *** proposed technique combines multiple traditional image-denoising algorithms and Convolutional Neural Network(CNN)network *** used detector model integrates the classification results of different models as the input to the detector and calculates the final output of the detector based on a machine-learning voting *** analyzing the discrepancy between predictions made by the model on original examples and denoised examples,AEs are detected *** technique reduces computational overhead without modifying the model structure or parameters,effectively avoiding the error amplification caused by *** proposed approach demonstrates excellent detection performance against mainstream AE *** results show outstanding detection performance in well-known AE attacks,including Fast Gradient Sign Method(FGSM),Basic Iteration Method(BIM),DeepFool,and Carlini&Wagner(C&W),achieving a 94%success rate in FGSM detection,while only reducing the accuracy of clean examples by 4%.

关键词： Deep neural networks adversarial example image denoising adversarial example detection machine learning adversarial attack

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