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Evaluating SDN applicability in the Edge

Evaluating SDN applicability in the Edge

引用

ieee international conference on Communications (ieee ICC)

作者： Liatifis, Athanasios Lagkas, Thomas Katsikas, Georgios P. Bujari, Armir Argyriou, Vasileios Triantafyllou, Anna Lytos, Anastasios Boulogeorgos, Alexandros-Apostolos A. Sarigiannidis, Panagiotis Univ Western Macedonia Dept Elect & Comp Engn Kozani 50100 Greece Int Hellen Univ Dept Comp Sci Kavala Campus Kavala 65404 Greece UBITECH Ltd Thessalias 8 & Etolias 10 Athens 15231 Greece Univ Bologna Dept Comp Sci & Engn Viale Risorgimento 2 I-40136 Bologna Italy Kingston Univ London Dept Networks & Digital Media Penrhyn Rd Kingston Upon Thames KT1 2EE Surrey England Sidroco Holdings Ltd Nicosia Cyprus

ISBN: (纸本)9798350333077

Multi-Access Edge computing (MEC) and Software Defined Networking (SDN) are novel paradigms, often utilised together, and aim to simplify the management of resources and offer cloud capabilities at the edge. SDN was originally designed for complex and large networks though recently found applications in smaller networks with fewer links. Little effort is given to the evaluation of SDN applicability at the Edge, especially in scenarios where Internet of Things (IoT) deployments are involved. This work presents an evaluation of SDN technology applicability at the edge in terms of data plane and control plane resource consumption. Our findings justify the wide acceptance of SDN and show that the control plane consumes relatively few resources compared to MEC resource pool. However, given the current trends of SDN, there are several possible improvements with respect to the cost of new equipment and the availability of tools.

关键词： Multi-Access Edge computing (MEC) Software Defined Networking (SDN) Internet of Things (IoT)

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Cloud computing Pattern Identification with Machine Learning: A Study

Cloud Computing Pattern Identification with Machine Learning...

引用

2023 ieee international conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering, RMKMATE 2023

作者： Jindal, Pardeep Kumar Srivastava, Durgesh Kumar, Mirgender Kaur, Manbir Sharma, Preeti Chandigarh Engineering College Jhanjeri Mohali India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350305708

Delivering needed computer resources over the Internet has made cloud computing an increasingly important requirement. Automated methods to find broad trends in cloud systems' usage and behaviour are becoming increasingly necessary as they get more complicated. The artificial intelligence subfield of machine learning provides an intriguing solution to this issue. The methods for detecting performance abnormalities, forecasting resource usage, and optimizing cloud system configurations are the main topics of this paper's review of recent work on machine learning for general pattern recognition in cloud computing. We also talk about the shortcomings and difficulties of the current methods and offer ideas for further research in this field. © 2023 ieee.

关键词： Artificial intelligence cloud computing machine learning

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Novel Architecture Integrating XAI with Blockchain and IoT Devices for Healthcare 2

Novel Architecture Integrating XAI with Blockchain and IoT D...

引用

2nd international conference on Emerging trends in Information Technology and Engineering, ic-ETITE 2024

作者： Murthy, Ch.V.N.U. Bharathi Shri, M. Lawanya VIT Vellore Department of Computer Applications Vellore India VIT Vellore Department of Soft Computing Vellore India

ISBN: (纸本)9798350328202

As we know, change is the only constant present in healthcare services. In this rapidly developing world, the need to drastically improve healthcare performance is essential. Real-time health data monitoring, analysis, and storage securely present us with a highly efficient healthcare system to diagnose, predict, and prevent deadly diseases. Integrating IoT data with blockchain storage technology gives safety and security to the data. The current bottleneck we face while integrating blockchain and IoT is primarily interoperability, scalability, and lack of regulatory frameworks. By integrating Explainable AI into the system, it is possible to overcome some of these bottlenecks between IoT devices and blockchain. XAI acts as a middleware solution, helping in interpreting the predictions and enforcing the standard data communication protocol. © 2024 ieee.

关键词： Middleware

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Bolstering the Mobile Cloud: Addressing Emerging Threats and Strengthening Multi-Layered Defenses for Robust Mobile Security 10

Bolstering the Mobile Cloud: Addressing Emerging Threats and...

引用

10th international conference on Internet of Things - Systems, Management and Security (IOTSMS)

作者： Ilapakurthy, Sriram Vamsi Univ Calif Irvine Samueli Sch Engn Irvine CA 92717 USA

ISBN: (纸本)9798350393897

The proliferation of mobile devices as the primary computing platform for billions of users worldwide introduces escalating security threats, especially as mobile interactions with sensitive data and cloud services increase. This paper provides an analysis of emerging mobile security issues including malware stealing credentials and data from mobile cloud services, lower-level operating system and hardware vulnerabilities enabling exploits on mobile cloud confidentiality and integrity, weaknesses in mobile apps enabling attacks, and rising user privacy risks from excessive tracking. current security limitations are also reviewed, including signature-based malware detection, coarse-grained access control, and fragmented visibility across the mobile ecosystem. An integrated cross-layer security strategy is proposed combining formal verification of critical mobile components, pervasive hardware-anchored security, intelligence-driven malware adaptation, robust devsecops processes, and coordinated defense visibility. As mobile continues dominating personal and enterprise computing, constructing resilient security foundations through research and awareness of mobile security issues and countermeasures is imperative for realizing the potential of mobile cloud computing.

关键词： Mobile security Mobile malware Mobile vulnerabilities Mobile exploits Mobile cloud computing Mobile hardware security advanced mobile defenses Integrated security strategies

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Proceedings - 2024 ieee/ACM international conference on Big Data computing, Applications and Technologies, BDCAT 2024

Proceedings - 2024 IEEE/ACM International Conference on Big ...

引用

11th ieee/ACM international conference on Big Data computing, Applications and Technologies, BDCAT 2024

ISBN: (纸本)9798350367300

The proceedings contain 57 papers. The topics discussed include: machine learning-based big data analytics in smart cities: a survey of current trends and future research directions;attribute-based semantic type detection and data quality assessment;exploring the knowledge mismatch hypothesis: hallucination propensity in small models fine-tuned on data from larger models;an explanation technique for yield prediction in smart farming;optimizing domestic energy consumption: a comprehensive plug for enhanced monitoring and efficiency;an accurate salary estimation scheme by using bigdata technique;a novel transfer learning approach for detecting partial shading in photovoltaic systems;extracting health evidence information from biomedical literature using large language models;semantic communications for healthcare applications: opportunities and challenges;plant diseases recognition using machine learning algorithms;unusual invoice detection using a permutation based genetic algorithm;an efficient iris recognition technique through minimal preprocessing and custom CNN;a cloud-agnostic serverless architecture for distributed machine learning;age-friendly trip planning using ant colony optimization;and temporal dynamics and anomaly detection in transactional networks.

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A Comprehensive Survey: Exploring current trends and Challenges in Intrusion Detection and Prevention Systems in the Cloud computing Paradigm 2

A Comprehensive Survey: Exploring Current Trends and Challen...

引用

2nd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Prabu, K. Sudhakar, P. School of Computing Science and Engineering Galgotias University Greater Noida India

ISBN: (纸本)9798350327533

The rapid evolution of internet technologies has led to a significant proliferation of connected devices, expanding the potential attack surface. This necessitates the implementation of effective countermeasures to safeguard network infrastructures against the detrimental impacts of cyber-Attacks. Hence, it is crucial to differentiate personal information and cloud computing worldwide, and to implement specific policies and regulations for information security. The main aim of the security policy and framework in cloud computing is safeguarding users and data, optimizing task operations, ensuring compliance, and setting user behavior standards. Additionally, IDPS-Intrusion Detection and Prevention Systems have gained widespread adoption as solutions for monitoring and analyzing network traffic, identifying anomalies, thereby detecting and preventing the ongoing adversarial activities, which play a crucial role in triggering alerts and autonomously blocking traffic from malicious sources. This study provides a comprehensive outline for a security policy in cloud computing with a focus on critical security tools, policies, and services, placing a particular emphasis on cloud environments to meet the organizational adoption requirements. By establishing vital connections between prerequisites, legal considerations, analytical techniques, and IDPS to mitigate threats, this research study provides strategic recommendations for cloud computing security policies. © 2024 ieee.

关键词： Cloud computing Cloud Security Information Privacy Intrusion Detection System (IDS) Intrusion Prevention System (IPS) Network Security Security Policies

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Enhancing Decision-Making and Operational Efficiency Through Demand Prediction Using Machine Learning 4

Enhancing Decision-Making and Operational Efficiency Through...

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4th international conference on Emerging trends in Networks and Computer Communications, ETNCC 2024

作者： Bahadure, Nilesh Bhaskarrao Singh, Neelam Patni, Jagdish Chandra Durge, Rutuja Pipalatkar, Shailaja Khomane, Ramdas Choudhury, Tanupriya Deshmukh, Saumya Nagpur Campus Pune India Graphic Era Deemed to be University Dept. of CSE Dehradun India Alliance School of Advanced Computing Alliance University Deptt. of CSE Bengaluru India Uttarakhand Dehradun248007 India

ISBN: (纸本)9798350353266

Launching a new product in the market and then getting reviews about the product so that the feasibility of the product can be planned accordingly mainly depends on the survey information or data from the sources. Here, decision-making plays a significant contribution, and this is where demand prediction comes into the picture. Demand prediction will not only help in planning the business and optimizing the operations but will also help get more information about the product's performance in the market. This study mainly focuses on how machine learning algorithms and other techniques can be used to predict the demand for the product. The model proposed in this study can enhance decision-making and efficiency and lead to high-standard market performance. © 2024 ieee.

关键词： Adversarial machine learning

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Utilizing Resource Estimation for the Development of Quantum computing Applications 5

Utilizing Resource Estimation for the Development of Quantum...

引用

2024 international conference on Quantum computing and Engineering

作者： Quetschlich, Nils Soeken, Mathias Murali, Prakash Wille, Robert Tech Univ Munich Chair Design Automat Munich Germany Microsoft Quantum Zurich Switzerland Univ Cambridge Cambridge England Software Competence Ctr Hagenberg GmbH SCCH Hagenberg Austria

ISBN: (纸本)9798331541378

Quantum computing has made considerable progress in recent years in both software and hardware. But to unlock the power of quantum computers in solving problems that cannot be efficiently solved classically, quantum computing at scale is necessary. Unfortunately, quantum simulators suffer from their exponential complexity and, at the same time, the currently available quantum computing hardware is still rather limited (even if roadmaps make intriguing promises). Hence, in order to evaluate quantum computing applications, end-users are still frequently restricted to toy-size problem instances (which additionally often do not take error correction into account). This substantially hinders the development and assessment of real-world quantum computing applications. In this work, we demonstrate how to utilize Resource Estimation to improve this situation. We show how the current workflow (relying on simulation and/or execution) can be complemented with an estimation step, allowing that end-users (1) actually can consider real-world problem instances already today (also considering error correction schemes and correspondingly required hardware resources), (2) can start exploring possible optimizations of those instances across the entire design space, and (3) can incorporate hypotheses of hardware development trends to derive more informed and, thus, better design space parameters. Overall, this enables end-users already today to check out the promises of possible future quantum computing applications, even if the corresponding hardware to execute them is not available yet.

关键词： Quantum computers

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Big Data with Cloud computing Model for Customer Need Identification in E-Commerce Industry 5

Big Data with Cloud Computing Model for Customer Need Identi...

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5th ieee international conference on Recent trends in Computer Science and Technology, ICRTCST 2024

作者： Mahajan, Kirti Bordoloi, Dibyhash Barboza, Coral Bansal, Divya Madhava Rao, B. Sri Varshini, S. Bharati Vidyapeeth Deemed University Institute of Management & Entrepreneurship Development Department of Information Technology Pune411030 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Amity University Department of Management Noida India St. Martins Engineering College Department of Computer Science and Engineering Telangana Secunderabad500100 India New Horizon College of Engineering Department of Mechanical Engineering Bangalore560103 India

ISBN: (纸本)9798350351378

In this study, we investigate how Big Data and Cloud computing can work together to better understand what online shoppers really want. The proliferation of e-commerce has increased the importance of listening to and meeting the requirements of customers. This research utilizes a wide variety of data sources to perform descriptive and predictive analysis, including customer transaction records, website clickstream data, customer reviews, and social media interactions. The prediction model's impressive prognostic accuracy may be attributed to its incorporation of client demographics, historical purchase history, and sentiment analysis. Text mining and natural language processing (NLP) are proven to provide insights from consumer input, while cloud computing architecture is shown to increase operational efficiency and scalability. In this paper, we explore how E-commerce enterprises may benefit from this game-changing technology by improving their customer service, cutting costs, and gaining an edge in the market. Business leaders, data scientists, cloud providers, academics, and the public all stand to gain from this study's findings. Future research directions that hold promise for keeping the E-commerce landscape at the cutting edge of innovation and customer-centric strategies include advanced deep learning techniques;real-time customer need identification;data privacy;scalability;cross-channel analysis;and industry benchmarking. © 2024 ieee.

关键词： Big data

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Comparative Analysis of Workforce Adaptation and Job Evolution in the Age of Automation Using Machine Learning 4

Comparative Analysis of Workforce Adaptation and Job Evoluti...

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4th international conference on Emerging trends in Networks and Computer Communications, ETNCC 2024

作者： Bahadure, Nilesh Bhaskarrao Pandey, Pramod Bhutda, Aryan Kumbhalkar, Rishikesh Nittala, Aditya Patni, Jagdish Chandra Mohiddin, Md. Khaja Nagpur Campus Pune India School of Advance Computing Alliance University CSE Bengaluru India BIT Department of ET & T Chhattisgarh Raipur India

ISBN: (纸本)9798350353266

This study explores the transformative effects of automation on the global workforce, focusing on job displacement and creation from 2020 to 2030. Utilizing a comprehensive analysis of data from the McKinsey Global Institute report and additional scholarly sources, we examine the varying impacts across different sectors, including manufacturing, healthcare, retail, and technology. Through quantitative methods, including regression models, and qualitative insights, the study highlights significant shifts in job types and skill demands. We assess the economic implications of these changes, proposing strate-gic recommendations for policymakers, educators, and business leaders to navigate this transition. The findings suggest that while automation presents substantial challenges by displacing routine jobs, it also offers opportunities for economic growth and job creation in new areas requiring advanced cognitive and social-emotional skills. © 2024 ieee.

关键词： Job analysis

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