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Mitigation for cloud computing security risks and governance

International Journal of Cloud Computing 2022年第5-6期11卷 560-567页

作者： Jabez, J. Narmadha, R. Porkodi, S. Devi, L. School of Computing Sathyabama Institute of Science and Technology Chennai India School of Electrical and Electronics Engineering Sathyabama Institute of Science and Technology Chennai India College of Engineering and Computer Science Lebanese French University Erbil Iraq PGCS Department Muthayammal College of Arts and Science Rasipuram India

The recent prosperous of cloud computing has become the biggest revenue for originalities with the issues of intricate servers that need regular maintenance, security, power and cooling systems throughout. Hence, it is extremely significant for any cloud service earner is to offer dense security models and policies to all users involves. Although creating best suited security strategies might necessitate enormous amount of reserve and endurance, it will bounce the casual for end user to relay on a computerised security system throughout. The cloud environs supplementary strongly secure, qualifying security hazards, appropriate controls must be imposed. Major contest is to consider cloud security and governance explanation together. In this segment, the emphasis will be in the contests involved in preserving a dependable security policy computerisation in at the application layer, network layer and host level of cloud computing. Moreover, securities related to models will be discussed to recruit for those security applications which are more composite and active. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Security systems

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Breast Cancer Classification using Metaheuristic Optimization and Machine Learning 3

Breast Cancer Classification using Metaheuristic Optimizatio...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Chandra, Avinash Kadam, Ayush Sangram Jain, Palak Kumawat, Mohit Patil, Hemprasad Yashwant Gawas, Mahadev Anant Vellore Institute of Technology School of Electronics Engineering Vellore India Vellore Institute of Technology School of Computer Science and Engineering Vellore India State Higher Education Council Directorate of Higher Education State Government of Goa Goa India

ISBN: (纸本)9798350372120

This research utilizes advanced machine learning techniques, specifically focusing on Decision Tree, Naive Bayes, Random Forest, and Ada Boost models, to conduct a thorough examination of breast cancer prognosis. Our findings reveal compelling insights into the effectiveness of diverse machine learning models in assessing breast cancer. The Decision Tree model achieves an impressive accuracy of 93.5%, accompanied by a precision rate of 86.73%. The Naive Bayes model demonstrates high accuracy, reaching 95.5%, with a precision of 91.32%. Notably, the Random Forest model stands out with exceptional performance, achieving an impressive accuracy of 98.3% and a precision of 96.5%. The Ada Boost model also proves to be robust, boasting an accuracy of 97.0% and a precision of 93.37%. After performing metahueristic hyperparameter tuning with differential evolution algorithm and derived feature engineering we have achieved accuracy of 98.83% and 98.41% precision. This study serves as a valuable resource for healthcare professionals, researchers, and policymakers, providing essential insights into the strengths and limitations of various machine learning models in the context of breast cancer prognosis. © 2024 IEEE.

关键词： Decision trees

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Correction to: Optimized auxiliary physics neural network based multi‑target detection for edge‑cloud in smart internet of things

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Peer-to-Peer Networking and Applications 2025年第3期18卷 1-2页

作者： Prabha, B. Yaramadhi, Mallikarjun Sharma, Abhishek Padmanaban, K. School of Computer science and Engineering Vellore Institute of Technology Chennai India Department of Computer Science and Engineering Institute of Aeronautical Engineering Hyderabad India Department of Computer Science Engineering SVIIT ShriVaishnavVidyapeethVishwavidyalaya Indore India Department of Computer Science and Engineering KoneruLakshmaiah Education Foundation Vaddeswaram India

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Investigation of Task Scheduling in Cloud Computing by using Imperialist Competitive and Crow Search Algorithms 3

Investigation of Task Scheduling in Cloud Computing by using...

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3rd International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2023

作者： Ravi, Jayavadivel Rajkumar, N. Viji, C. Loganathan, D. Sushma, K.S.N. Stalin, M. Department of Computer Science & Engineering Alliance College of Engineering and Design Alliance University Karnataka Bangalore India Department of Information Science & Engineering Cambridge Institute of Technology Karnataka Bangalore India School of Computer Science and Engineering REVA University Karnataka Bangalore India

Cloud Storage is a complex method that is a method of processing and data of a cloud built by duplication of thousands of related devices in a complex manner. The main function of the data processing server is to show how many users are being investigated and provide accurate, efficient and efficient information. Important Algorithm Editing Players in the Cloud Defines the virtual machine (VM) required for this purpose. The role of editing the algorithm reduces the effect of the schedule. Naturally affected algorithms have recently been used to quickly comply with the recent traditional algorithms. Given many consumers of many cloud computing services, many researchers may have a serious explanation that many researchers take and discuss the complex themes of NP. Some sites use imperialist algorithms (ICA) and birds. The purpose of the proposed project is to develop intelligent scientific algorithms that focus on the integration of ICA and CSA to obtain data. CSA is concentrated on the corner of food habits. The crow is looking for his friends to get enough food for today's food. This will help the CSA find suitable VMs for these machines and complete the equipment. Cloud Sim is used to calculate CSA output with minmin and ant algorithms. The simulation results show that the CSA is extra powerful than the MinMin and Ant procedures. © 2023 Elsevier B.V.. All rights reserved.

关键词： Cloud computing

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A Security Enhanced Chaotic-Map-Based Authentication Protocol for Internet of Drones

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IEEE Internet of Things Journal 2024年第12期11卷 22301-22309页

作者： Mahmood, Khalid Ghaffar, Zahid Farooq, Muhammad Yahya, Khalid Das, Ashok Kumar Chaudhry, Shehzad Ashraf National Yunlin University Of Science And Technology Graduate School Of Intelligent Data Science Douliu64002 Taiwan National Yunlin University Of Science And Technology Graduate School Of Engineering Science And Technology Douliu64002 Taiwan Abu Dhabi University Department Of Mathematics And Statistics College Of Arts And Sciences Abu Dhabi United Arab Emirates Istanbul Gelisim University Department Of Electrical And Electronics Engineering Istanbul34310 Turkey Sabratah University Department Of Electrical Engineering Faculty Of Engineering Regdalen Libya Center For Security Theory And Algorithmic Research Hyderabad500 032 India Abu Dhabi University Department Of Computer Science And Information Technology College Of Engineering Abu Dhabi United Arab Emirates Nisantasi University Faculty Of Engineering And Architecture Department Of Software Engineering Istanbul34398 Turkey

The Internet of Drones (IoD) extends the capabilities of unmanned aerial vehicles, enabling them to participate in a connected network. In IoD infrastructure, drones communicate not only among themselves but also with users and a control center. This interconnected communication framework holds promise for various applications, from collaborative decision-making to real-time data exchange. However, the expansion of communication in IoD also introduces new challenges, particularly in terms of security, privacy and authentication. Unfortunately, the current authentication protocols are inadequate in offering robust security features against various attacks in the IoD environment. To address these security issues and limitations, we proposed a secure protocol for the IoD environment using chaotic maps and hash functions. In addition, we also employed a physically unclonable function in the development of the proposed protocol. We assess the security of the protocol through both informal and formal security analysis. The formal security analysis is conducted through a widely used random or real (RoR) model. The informal analysis shows the rigorous security features against various attacks, such as masquerading, anonymity violation, and physical cloning attacks. Moreover, we compare the performance of the devised protocol with similar existing protocols across important performance parameters, such as communication overhead, computation overhead, and security features. The devised protocol provides a 67.86% and 17.80% reduction in computation and communication overheads, respectively, as compared to related protocols. The analysis demonstrates the proposed protocol's capacity to support secure communication in the IoD environment and satisfy desirable security attributes. © 2014 IEEE.

关键词： Drones

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Integrative Remote Sensing Approaches Using Generative Adversarial Networks for Urban Heat Island Analysis and Mitigation

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Remote Sensing in Earth Systems sciences 2024年第4期7卷 681-698页

作者： Sundar, Ganapathy Patchaiammal, Pitchaimuthu Venkateshwarulu, Balaji Vijayan Kumar, Thangavel Pradeesh Rajamannar, Kesavamoorthy Tripathi, Rajesh Kumar Department of Computer Applications Sindhi College Chennai India Department of Information Science and Engineering HKBK College of Engineering VTU Belagum Bengaluru Nagawara India VIT School of Agricultural Innovations and Advanced Learning (VAIAL) VIT Vellore India Department of Computer Science and Engineering CMR Institute of Technology Bengaluru India Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura India

The phenomenon of urban heat islands (UHI) presents a critical challenge for urban sustainability, exacerbating local temperatures, increasing energy demands, and impairing public health. Traditional methods for addressing UHI are often resource-intensive and slow. This study introduces a novel approach, utilizing a Hybrid CycleGAN-SVM (HCGS) model that leverages the synergy of Generative Adversarial Networks (GANs) and Support Vector Machines (SVMs) to efficiently analyze and mitigate UHI effects through high-resolution satellite imagery and temperature data. The model incorporates Enhanced Vision Transformers (EViTs) for superior feature extraction, adept at capturing intricate spatial and spectral urban patterns. The CycleGAN component of the model generates high-quality synthetic imagery, enhancing the dataset and addressing class imbalances, thereby bolstering the SVM classifier’s ability to precisely pinpoint heat-prone urban areas. Implemented in Google Colab, the HCGS model demonstrated exceptional performance, achieving a classification accuracy of 0.98. This indicates its potential as an effective tool for urban heat mitigation, offering actionable insights for urban planning and policy-making. By integrating advanced machine learning techniques with remote sensing data, the HCGS model paves the way for innovative climate adaptation strategies, fostering more sustainable and resilient urban environments. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Enhanced Vision Transformers EViTs GANs Generative Adversarial Networks HCGS model Hybrid CycleGAN-SVM Satellite imagery Support Vector Machines SVM UHI effect Urban heat island

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An Adaptive Explainable AI Framework for Securing Consumer Electronics-Based IoT Applications in Fog-Cloud Infrastructure

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 1-1页

作者： Tripathy, Subhranshu Sekhar Guduri, Manisha Chakraborty, Chinmay Bebortta, Sujit Pani, Subhendu Kumar Mukhopadhyay, Sabyasachi School of Computer Engineering KIIT Deemed to be University Bhubaneswar India University of Louisiana at Lafayette USA Department of Electronics and Communication Engineering Birla Institute of Technology Jharkhand India Department of Computer Science Ravenshaw University Cuttack Odisha India Kausalya Ganga Bhubaneswar Odisha India Centre For Computational And Data Sciences Indian Institute of Technology Kharagpur Kharagpur West Bengal India

A prominent use case of consumer electronics-based Internet of Things (IoT) applications, focused on smart cities, is connected devices that enable cities to optimize their operations via access to high volumes of sensitive data. Yet, these devices commonly utilize public channels for data access and sharing, requiring consistent communication protocols and an Intrusion Detection System (IDS) with the aid of AI. However, most of them involve high computation and communication costs. They are not fully reliable, either. Also, AI-based IDS solutions are viewed as black boxes because they cannot justify their decisions. To resolve these issues, we have proposed a framework based on explainable artificial intelligence (XAI) for securing consumer IoT applications in smart cities. At the beginning of the protocol execution, the participants exchange authenticated data through the blockchain-based AKA procedure. Meanwhile, we adopt the Python-based Shapley Additive Explanation (SHAP) framework to explain and interpret the core features guiding decision-making. The working model of this framework depicts its validation with recent benchmark methods. IEEE

关键词： Energy efficiency

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Accurate and explainable machine learning for the power factors of diamond-like thermoelectric materials

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Journal of Materiomics 2022年第3期8卷 633-639页

作者： Zhe Yang Ye Sheng Cong Zhu Jianyue Ni Zhenyu Zhu Jinyang Xi Wu Zhang Jiong Yang Materials Genome Institute Shanghai UniversityShanghai200444China School of Computer Engineering and Science Shanghai UniversityShanghai200444China School of Mechanics and Engineering Science Shanghai UniversityShanghai200444China

The application of machine learning(ML)-based methods to the study of thermoelectric(TE)materials is *** conventional ML algorithms can achieve high prediction performance,their lack of interpretability severely obstructs researchers from extracting material-oriented insights from ML *** this work,high ML-based prediction performance was achieved with respect to TE power factors(PFs),and the results were well understood by the SHapley Additive exPlanations(SHAP),a method to identify the correlations between targets and *** designed a robust PF prediction model for diamond-like compounds via a stacking technique,and the model achieved a coefficient of determination value above 0.95 on the test *** the SHAP analysis,the PFs were negatively correlated with electronegativity and positively correlated with the descriptor“volume per atom”based on the previously reported *** domain knowledge was adopted to understand these *** work shows that ML models can achieve high accuracy while exhibiting good interpretability,making them useful for materials scientists.

关键词： Machine learning Thermoelectric materials Interpretability Power factor

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Design and implementation of vehicle type recognition system based on language large model

Design and implementation of vehicle type recognition system...

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Internet of Things, Automation and Artificial Intelligence (IoTAAI), International Conference on

作者： Xiang Liu Yikuan Zhang Computer Science Department Software Engineering Institute of GuangZhou Guangzhou China Department of Electronics Software Engineering Institute of GuangZhou Guangzhou China

ISBN: (数字)9798350386974

ISBN: (纸本)9798350386981

Using large language model to generate vehicle type recognition algorithm can reduce the burden of developers and realize the rapid development of projects. In this paper, LangChain large model interface provided by Baidu Qianfan large-scale platform is used to realize the auxiliary development of vehicle type recognition. With the support of large model, the training and testing model of vehicle type recognition is constructed by using deep learning method, and the fine-grained vehicle image classification and recognition system is realized. At present, the ResNet50 Enhance MEAL2 model is used for classification recognition, supporting 1748 types of vehicle classification and recognition. In the Car-Dataset of vehicle data, the Accuracy of the training set is about 95.9%, and the Accuracy of the test set is about 84.3%. The backbone network can support Googlenet. ResNet [18, 34, 50], Inception_v3, Mobilenet_v2 and other common models. The use of language large model in this project improves the efficiency of development.

关键词： Training Image recognition Accuracy Large language models Web and internet services Neural networks Internet of Things Testing Residual neural networks Image classification

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A Novel Approach of Human Stress Rate Monitoring System in Smart Society 5.0 1

A Novel Approach of Human Stress Rate Monitoring System in S...

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1st International Conference on Computing, sciences and Communications, ICCSC 2024

作者： Aggarwal, Ambika Joshi, Pooja Bhatt, Ashutosh Joshi, Kapil Joshi, Mayurika Mahur, Mansi School of Computer Science UPES Uttarakhand India HSST Swami Rama Himalayan University Department of Computer Science and Engineering Uttarakhand India Uttaranchal Institute of Technology Uttaranchal University Department of CSE Dehradun India Graphic Era hill university Department of computer science and engineering Haldwani India Infosys Hyderabad India

ISBN: (纸本)9798350353648

Over the course of the present years, the Internet of Things (IoT) made ways for developers which work with communications among people and things. Zeroing in on medical care space, devices like clinical detectors, visual detectors, cameras, and the remote detector network were driving that developmental pattern. Objective: Toward this path, these studies proposed a unique IoT-mindful understudy-driven pressure, observing structure to foresee an understudy pressure list in a specific setting. Method: Bayesian Network (BN) was utilized the characterize pressure occasion in a typical or unusual utilizing physiological readings gathered among the clinical detectors in the haze layer. Strange transient primary data that was time-advanced data file grouping was broken down at various pressure-related boundaries in the cloud layer. From the technique's understudy pressure record, a Dynamic Bayesian Network (DBN) of two stage design is framed. Results: This system figures pressure given four boundaries, in particular, leaf node confirmations, responsibility, and setting, also to understudy the wellbeing characteristic. In the wake of processing the pressure file based on the understudy, choices were taken as ready age components with the redemption of time-delicate data to the overseer or responder. Conclusion: Tests were led both at the haze and cloud layer that holds the proof in the usage also the exactness based on the BN classifier and the DBN prescient system in our proposed framework. © 2024 IEEE.

关键词： Electrotherapeutics

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