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Optimizing Resource Allocation Framework for Multi-Cloud Environment

computers, Materials & Continua 2023年第5期75卷 4119-4136页

作者： Tahir Alyas Taher M.Ghazal Badria Sulaiman Alfurhood Ghassan F.Issa Osama Ali Thawabeh Qaiser Abbas Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebansaan Malaysia(UKM)43600BangiSelangorMalaysia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia Skyline University College University City Sharjah1797SharjahUAE Faculty of Computer and Information Systems Islamic University of Madinah Madinah42351Saudi Arabia

Cloud computingmakes dynamic resource provisioning more *** a functioning service is crucial,and changes are made when particular criteria are *** research explores the decentralized multi-cloud environment for allocating resources and ensuring the Quality of Service(QoS),estimating the required resources,and modifying allotted resources depending on workload and parallelism due to *** allocation is a complex challenge due to the versatile service providers and resource *** engagement of different service and resource providers needs a cooperation strategy for a sustainable quality of *** objective of a coherent and rational resource allocation is to attain the quality of *** also includes identifying critical parameters to develop a resource allocation mechanism.A framework is proposed based on the specified parameters to formulate a resource allocation process in a decentralized multi-cloud *** three main parameters of the proposed framework are data accessibility,optimization,and *** an optimization technique,these three segments are further divided into subsets for resource allocation and long-term service *** CloudSim simulator has been used to validate the suggested *** experiments have been conducted to find the best configurations suited for enhancing collaboration and resource allocation to achieve sustained *** results support the suggested structure for a decentralized multi-cloud environment and the parameters that have been determined.

关键词： Multi-cloud query optimization cloud resources allocation modelling virtualization

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A Review on Crop Yield Prediction using Deep Learning

A Review on Crop Yield Prediction using Deep Learning

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International Conference on Inventive Systems and Control (ICISC)

作者： Sheily Verma Panwar Shubham Singh Department of computer Science/ Cyber security CUC-Ulster University Doha Qatar Era Smith Technology Noida India

ISBN: (数字)9798350386578

ISBN: (纸本)9798350386585

The integration of cutting-edge technology into agricultural practices has catalyzed a transformative shift in global agriculture. This research study focuses on the revolutionary impact of deep learning approaches on crop yield prediction, a critical aspect of sustainable agriculture amid increasing global food demands. Traditional methods for crop yield estimation are examined initially, revealing limitations in accuracy and adaptability to diverse agroclimatic conditions. Subsequently, the rise of deep learning is explored, highlighting its ability to autonomously extract complex patterns from vast agricultural datasets. This research study provides a comprehensive classification and analysis of deep learning architectures, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and attention mechanisms, in the context of agricultural yield prediction. This study discusses about the incorporation of sensor-based information, satellite imagery, and climate data into deep learning models, emphasizing the synergy among the data sources to enhance the prediction accuracy. Additionally, this research study addresses the challenges related to data quality, model interpretability, and scalability, proposing solutions and future research directions. By illuminating the transformative role of deep learning in crop yield prediction, this research study equips researchers, practitioners, and policymakers with valuable insights to advance sustainable agriculture and ensure global food security amidst changing climatic patterns and demographic trends.

关键词： Deep learning Recurrent neural networks Scalability Predictive models Feature extraction Prediction algorithms Agriculture

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AI-Enabled DDoS Detection and Mitigation in the Software Defined Network

AI-Enabled DDoS Detection and Mitigation in the Software Def...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： K. Deepthika T. Vanaja S. Keerthika Prajwalasimha S N Department of Computer Science and Engineering (Cyber Security) School of Engineering Dayananda Sagar University Bangalore India Kongu Engineering College Erode India

ISBN: (数字)9798350379945

ISBN: (纸本)9798350379952

Traditional networks typically employ a decentralized control plane, while Software Defined Networking (SDN), a dynamic networking space, adopts a centralized control system. SDN has many benefits, including precise control, optimized routing, and efficient resource management, making it well-suited for data-centric networks. Despite these benefits, SDNs are susceptible to traffic attack, and the development of robust detection and mitigation strategies. Addressing the challenge of attack with Distributed Denial of Service (DDoS) in SDN environment. This research proposes an OpenFlow-based detection approach for mitigation within the control plane. And employs a specific SDN DDoS attack dataset that incorporates unique features. When it comes to DDoS attack detection, deep learning and machine learning models are used for classifying these attacks. The outcomes reveal that a Neural Network demonstrates good performance among the algorithm used on this dataset, achieving an accuracy of 99.4%. Subsequently, the controller takes action blocking the malicious host, employing the addition of blocking rules to the switches.

关键词： Deep learning Accuracy Prevention and mitigation Denial-of-service attack Routing Feature extraction Resource management Software defined networking computer crime Centralized control

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An Exploratory Assessment on Text Summarization

An Exploratory Assessment on Text Summarization

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Advancements in Smart, Secure and Intelligent Computing (ASSIC), International Conference on

作者： P. Nishitha T. Sarika E. N. Vijayakumari Asha Vuyyuru M SilpaRaj Jnyana Ranjan Mohanty Department of Assistant Computer science and Information Technology MLR Institute of Technology Hyderabad Department of Computer science and Information Technology MLR Institute of Technology Hyderabad Department of Computer science Engineering MLR Institute of Technology Hyderabad Department of Computer Science and Engineering (AIML) BVRIT HYDERABAD College of Engineering for Women Hyderabad Department of Computer Science and Engineering (Cyber Security) CVR College of Engineering Hyderabad Kalinga Institute of Industrial Technology University India

Text summarization involves creating a short, concise summary that captures the salient points of the source text. We'll demonstrate a synthesis process based on the use of trainable machine learning algorithms with a collection of features taken straight from the source text. There are two groups of these features: statistics-also referred to as extractive summarization-based on how frequently different textual components occur and linguistics also known as abstractive summarization - based on morphological and syntactic analysis of documents. In this paper, we show how BERT can be usefully applied in text summarization and provide a common framework for abstraction models. We also demonstrate that a fine-tuning method can further improve the quality of the generated summaries.

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Traffic Sign Detection Problems using Convolutional Neural Techniques in Image Processing

Traffic Sign Detection Problems using Convolutional Neural T...

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2024 International Conference on Intelligent Systems for cybersecurity, ISCS 2024

作者： Subhamathi, A.S.F. Sathiyapriyan, C. Anuradha, T. Jose Anand, A. Rheem, Abdul Arun, M.R. Karpagam Academy of Higher Education Department of Electrical and Electronics Engineering Tamilnadu Coimbatore641021 India Aarupadai Veedu Institute of Technology Department of Computer Science and Engineering Cyber Security Chennai603104 India Kcg College of Technolog Department of Electrical and Electronics Engineering Tamil Nadu Chennai600097 India Kcg College of Technology Department of Ece Karapakkam Chennai600 097 India Mullana Department of Mathematics and Humanities Haryana Ambala India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai India

ISBN: (纸本)9798350375237

Traffic sign detection is a critical component in advancing intelligent transportation systems and ensuring road safety. Convolutional Neural Networks (CNNs) have emerged as powerful tools in image processing and computer vision, demonstrating remarkable success in various domains, including traffic sign recognition. This paper presents a comprehensive exploration of the challenges associated with traffic sign detection and the application of CNN techniques to address these issues. The paper reviews the traditional methods used for traffic sign detection and emphasizes the limitations that motivate the adoption of CNNs. The core of the paper delves into the application of convolutional neural techniques for traffic sign detection. Various CNN architectures are surveyed and highlight their strengths and weaknesses in handling specific challenges inherent to traffic sign recognition. Attention mechanisms, transfer learning, and other advancements in CNNs are explored for their potential to enhance detection accuracy and robustness. In conclusion, this paper contributes to the understanding of how convolutional neural techniques can be leveraged to tackle traffic sign detection challenges. © 2024 IEEE.

关键词： Object detection

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A Novel Key Management Framework for Secure and Scalable Decentralized Identity Systems

A Novel Key Management Framework for Secure and Scalable Dec...

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security of Information and Networks (SIN), International Conference on

作者： Mert Yıldız Şerif Bahtiyar SPFLab Department of Computer Engineering Cyber Security and Privacy Research Lab Istanbul Technical University Istanbul Türkiye

ISBN: (数字)9798331509736

ISBN: (纸本)9798331509743

The rise of decentralized identity systems has posed significant challenges in the secure and scalable management of keys, especially in large-scale national identity programs. In this paper, we propose a new secure and scalable framework for cryptographic keys management that may be applied in a national digital identity system. The proposed framework provides a hierarchical structure for efficient key generation and isolation, while hardware security modules provide a secure environment for key storage and operations. Key wrapping is implemented to enable secure external storage of large volumes of keys. In our work, we present a comprehensive security analysis. Our analysis demonstrates the resilience of the framework against various threat vectors and its ability to address key management challenges such as complexity, scalability, security isolation, recovery and secure delegation. The proposed framework provides a promising solution for security and scalability of national-level identity systems.

关键词： Scalability Hardware security Vectors Complexity theory Cryptography Wrapping Secure storage Resilience

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Towards Stricter Black-box Integrity Verification of Deep Neural Network Models 24

Towards Stricter Black-box Integrity Verification of Deep Ne...

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32nd ACM International Conference on Multimedia, MM 2024

作者： He, Chaoxiang Bai, Xiaofan Ma, Xiaojing Zhu, Bin B. Hu, Pingyi Fu, Jiayun Jin, Hai Zhang, Dongmei Huazhong University of Science and Technology Hubei Wuhan China Shanghai Jiao Tong University Shanghai China Microsoft Corporation Beijing China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9798400706868

Cloud-based machine learning services offer significant advantages but also introduce the risk of tampering with cloud-deployed deep neural network (DNN) models. Black-box integrity verification (BIV) allows model owners and end-users to determine if a cloud-deployed DNN model has been tampered with by examining only the top-1 label responses. Fingerprinting generates fingerprint samples to query the model, achieving BIV with no impact on the model's accuracy. In this paper, we present BIVBench, the first comprehensive benchmark for BIV of DNN models. BIVBench covers 16 types of model modifications, providing extensive coverage of practical modification scenarios. Our analysis reveals that existing fingerprinting methods, which are typically focused on significant tampering, lack the sensitivity needed to effectively detect subtle yet common and potentially severe modifications. To address this limitation, we propose MiSentry (Model Integrity Sentry), a novel fingerprinting method that leverages meta-learning. MiSentry strategically incorporates a few subtly modified models into the meta-learning model zoo and maximizes the divergence of output predictions between the target model and the modified models in the model zoo to generate highly sensitive, generalizable, and effective fingerprint samples. Extensive evaluations using BIVBench demonstrate that MiSentry outperforms existing state-of-the-art methods overall and significantly surpasses them in detecting subtle modifications. The BIVBench and supplementary materials are available at: https://***/CGCL-codes/BIVBench. © 2024 ACM.

关键词： Deep neural networks

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An Intelligent Approach to Spectrum Sharing Process for Coexistence Between Cellular and Wi-Fi Technologies

An Intelligent Approach to Spectrum Sharing Process for Coex...

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International Conference on Electrical engineering and Information & Communication technology (ICEEICT)

作者： Gazi Md Nasim Monir Hossen Moinul Hossain Department of Electronics and Communication Engineering Khulna University of Engineering & Technology Khulna Bangladesh Department of Cyber Security Engineering George Mason University USA

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

It is anticipated that New-Radio Unlicensed (NR-U) technology will offload its traffic to unlicensed spectrum, such as the 5 GHz band. Nevertheless, NR-U transmissions must coexist with the current Wi-Fi networks in the 5 GHz band. The majority of current coexistence plans take into account coordinated Wi-Fi and NR-U networks, where a centralized coordinator communicates the traffic demands of the co-located networks. However, using this technique to estimate Wi-Fi traffic increases the complexity, traffic overhead, and response times. Furthermore, the conventional Listen-Before- Talk (LBT) process of NR-U is unable to ensure enough fairness of the system. Therefore, an efficient coexistence mechanism must be required to improve the fairness. For this purpose, this paper presents an intelligent LBT based coexistence mechanism to maintain the fairness using Q-learning. In this proposed scheme, the NR-U base station optimizes its maximum channel occupancy time with respect to Wi-Fi access points, based on Q-learning algorithm to improve the system performance. We also make a performance comparison between the proposed and traditional LBT mechanisms. The simulation results show that our proposed mechanism provides better fairness compared to the conventional LBT mechanisms.

关键词： Training Q-learning Computational modeling System performance Simulation Throughput Decoding

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MineLoRa: Markov Transition Fields and Adaptive Neural Networks for LoRa Device Radio Frequency Fingerprint Identification

MineLoRa: Markov Transition Fields and Adaptive Neural Netwo...

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2024 International Conference on Artificial Intelligence of Things and Systems, AIoTSys 2024

作者： Zhu, Biaokai Jia, Yupeng Wang, Zhonghao Wen, Jiawei Liu, Xinle Zhao, Shijia Xu, Qiongyue Song, Jie Li, Ping Liu, Sanman Li, Feng Zhao, Jumin Shanxi Police College Taiyuan University of Technology Taiyuan China Shanxi Police College Cyber Security Department Taiyuan China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College Luzhou China School of Computer Science and Technology Anhui University Hefei China Taiyuan University of Technology Taiyuan China College of Information and Computer Taiyuan University of Technology Jinzhong China

ISBN: (纸本)9798331507886

With the development of LPWAN technology, LoRa, one of the typical representatives, is widely used in various communication scenarios. At the same time, it is also faced with problems such as difficulty in identifying the terminal equipment and the easy mistake and confusion. The industry has widely valued LoRa RF fingerprint recognition technology, which has become a security focus that cannot be ignored. Most recent research on terminal recognition has been carried out by multi-class feature fusion. Still, it needs help with high computational complexity, significant noise and other external interference factors, and harrowing feature extraction. We propose a time-frequency feature analysis method based on GAF and MTF. The time-domain features are transformed into two-dimensional images using the Gram Angle field and Markov transfer field. The spectral graph has both time-domain and frequency-domain characteristics, and the feature probability distribution in the images is intuitively analyzed. Finally, we design a deep neural network model based on CNN improvement. It can accurately extract and classify the features contained in multi-Time frequency. Our experiments show that a 97% recognition rate can be achieved in an indoor environment, and a 90% recognition accuracy can be achieved in an outdoor environment in time. © 2024 IEEE.

关键词： Frequency domain analysis

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Optimizing Hyperparameters in Deep Learning Algorithms for Self-Driving Vehicles in Traffic Sign Recognition

Optimizing Hyperparameters in Deep Learning Algorithms for S...

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Science, technology, engineering and Management (ICSTEM), International Conference on

作者： T. Kumaravel V. Shanmugaveni P. Natesan V. Kavya Shruthi M. Kowsalya M S. Malarkodi Department of Computer Science and Engineering Kongu Engineering College Erode Tamil Nadu India Department of CSE(Cyber Security) Dr.Mahalingam College of Engineering and Technology Pollachi Tamil Nadu India

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

Traffic sign detection is a pivotal aspect of intellect transportation systems, which play a crucial role in enhancing road safety along with traffic management. The utilization of the YOLOv5 framework provides a holistic solution for determining independently as well as recognizing road symbols. This approach was assessed using the German Traffic Sign Recognition Benchmark (GTSRB), a comprehensive data set comprising over 50,001 images across 43 distinct traffic sign classes. The model achieved an impressive testing accuracy of 95.31%. This abstract investigates the efficacy of the Pelican Optimization Algorithm (POA) and Cuckoo Search Algorithm (CSA) in fine-tuning hyperparameters for traffic sign detection. POA draws inspiration from the collaborative hunting behavior of pelicans, employing a swarm intelligence approach to adjust parameters such as learning rates, network architecture, and augmentation techniques. Likewise, CSA, inspired by the brood parasitism of cuckoo birds, utilizes Levy flights and random walks to effectively explore complex parameter spaces, finding an equilibrium among exploration and extraction. identify optimal model configurations. Both algorithms demonstrate proficiency in optimizing critical parameters essential for traffic sign detection, encompassing network architecture, training parameters, and data augmentation strategies. They adept exploration mechanisms and collaborative methodologies contribute to bolstering the accuracy and reliability of traffic symbol identification systems. In conclusion, the strategic utilization of metaheuristic optimization algorithms like POA and CSA significantly enhances the performance of traffic symbol identification by adeptly fine-tuning hyperparameters. These algorithms serve as valuable tools in optimizing detection models for real-world applications, with opportunities for further refinement and exploration of hybrid methodologies to maximize efficacy.

关键词： YOLO Training Accuracy Symbols Collaboration Transportation Network architecture

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