Future Internet最新文献_第5页

Analyzing GPU Performance in Virtualized Environments: A Case Study 分析虚拟化环境中的 GPU 性能：案例研究

Future Internet Pub Date : 2024-02-23 DOI: 10.3390/fi16030072

Adel Belkhiri, Michel Dagenais

引用次数: 0

Scheduling of Industrial Control Traffic for Dynamic RAN Slicing with Distributed Massive MIMO 利用分布式大规模多输入多输出（MIMO）调度工业控制流量，实现动态 RAN 分片

Future Internet Pub Date : 2024-02-23 DOI: 10.3390/fi16030071

Emma Fitzgerald, Michal Pióro

{"title":"Scheduling of Industrial Control Traffic for Dynamic RAN Slicing with Distributed Massive MIMO","authors":"Emma Fitzgerald, Michal Pióro","doi":"10.3390/fi16030071","DOIUrl":"https://doi.org/10.3390/fi16030071","url":null,"abstract":"Industry 4.0, with its focus on flexibility and customizability, is pushing in the direction of wireless communication in future smart factories, in particular, massive multiple-input-multiple-output (MIMO) and its future evolution of large intelligent surfaces (LIS), which provide more reliable channel quality than previous technologies. At the same time, network slicing in 5G and beyond systems provides easier management of different categories of users and traffic, and a better basis for providing quality of service, especially for demanding use cases such as industrial control. In previous works, we have presented solutions for scheduling industrial control traffic in LIS and massive MIMO systems. We now consider the case of dynamic slicing in the radio access network, where we need to not only meet the stringent latency and reliability requirements of industrial control traffic, but also minimize the radio resources occupied by the network slice serving the control traffic, ensuring resources are available for lower-priority traffic slices. In this paper, we provide mixed-integer programming optimization formulations for radio resource usage minimization for dynamic network slicing. We tested our formulations in numerical experiments with varying traffic profiles and numbers of nodes, up to a maximum of 32 nodes. For all problem instances tested, we were able to calculate an optimal schedule within 1 s, making our approach feasible for use in real deployment scenarios.","PeriodicalId":509567,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140438195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Learning for Intrusion Detection Systems (IDSs) in Time Series Data 时间序列数据入侵检测系统（IDS）的深度学习

Future Internet Pub Date : 2024-02-23 DOI: 10.3390/fi16030073

Konstantinos Psychogyios, Andreas Papadakis, S. Bourou, Nikolaos P. Nikolaou, Apostolos Maniatis, T. Zahariadis

{"title":"Deep Learning for Intrusion Detection Systems (IDSs) in Time Series Data","authors":"Konstantinos Psychogyios, Andreas Papadakis, S. Bourou, Nikolaos P. Nikolaou, Apostolos Maniatis, T. Zahariadis","doi":"10.3390/fi16030073","DOIUrl":"https://doi.org/10.3390/fi16030073","url":null,"abstract":"The advent of computer networks and the internet has drastically altered the means by which we share information and interact with each other. However, this technological advancement has also created opportunities for malevolent behavior, with individuals exploiting vulnerabilities to gain access to confidential data, obstruct activity, etc. To this end, intrusion detection systems (IDSs) are needed to filter malicious traffic and prevent common attacks. In the past, these systems relied on a fixed set of rules or comparisons with previous attacks. However, with the increased availability of computational power and data, machine learning has emerged as a promising solution for this task. While many systems now use this methodology in real-time for a reactive approach to mitigation, we explore the potential of configuring it as a proactive time series prediction. In this work, we delve into this possibility further. More specifically, we convert a classic IDS dataset to a time series format and use predictive models to forecast forthcoming malign packets. We propose a new architecture combining convolutional neural networks, long short-term memory networks, and attention. The findings indicate that our model performs strongly, exhibiting an F1 score and AUC that are within margins of 1% and 3%, respectively, when compared to conventional real-time detection. Also, our architecture achieves an ∼8% F1 score improvement compared to an LSTM (long short-term memory) model.","PeriodicalId":509567,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140436683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Ontology-Based Cybersecurity Framework for AI-Enabled Systems and Applications 基于本体的人工智能系统和应用网络安全框架

Future Internet Pub Date : 2024-02-22 DOI: 10.3390/fi16030069

Davy Preuveneers, Wouter Joosen

{"title":"An Ontology-Based Cybersecurity Framework for AI-Enabled Systems and Applications","authors":"Davy Preuveneers, Wouter Joosen","doi":"10.3390/fi16030069","DOIUrl":"https://doi.org/10.3390/fi16030069","url":null,"abstract":"Ontologies have the potential to play an important role in the cybersecurity landscape as they are able to provide a structured and standardized way to semantically represent and organize knowledge about a domain of interest. They help in unambiguously modeling the complex relationships between various cybersecurity concepts and properties. Leveraging this knowledge, they provide a foundation for designing more intelligent and adaptive cybersecurity systems. In this work, we propose an ontology-based cybersecurity framework that extends well-known cybersecurity ontologies to specifically model and manage threats imposed on applications, systems, and services that rely on artificial intelligence (AI). More specifically, our efforts focus on documenting prevalent machine learning (ML) threats and countermeasures, including the mechanisms by which emerging attacks circumvent existing defenses as well as the arms race between them. In the ever-expanding AI threat landscape, the goal of this work is to systematically formalize a body of knowledge intended to complement existing taxonomies and threat-modeling approaches of applications empowered by AI and to facilitate their automated assessment by leveraging enhanced reasoning capabilities.","PeriodicalId":509567,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139957944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Future of Healthcare with Industry 5.0: Preliminary Interview-Based Qualitative Analysis 工业 5.0 带来的医疗保健未来：基于访谈的初步定性分析

Future Internet Pub Date : 2024-02-22 DOI: 10.3390/fi16030068

Juliana Basulo-Ribeiro, Leonor C. Teixeira

引用次数: 0

Micro-FL: A Fault-Tolerant Scalable Microservice-Based Platform for Federated Learning Micro-FL：基于容错可扩展微服务的联合学习平台

Future Internet Pub Date : 2024-02-22 DOI: 10.3390/fi16030070

Mikael Sabuhi, Petr Musilek, C. Bezemer

{"title":"Micro-FL: A Fault-Tolerant Scalable Microservice-Based Platform for Federated Learning","authors":"Mikael Sabuhi, Petr Musilek, C. Bezemer","doi":"10.3390/fi16030070","DOIUrl":"https://doi.org/10.3390/fi16030070","url":null,"abstract":"As the number of machine learning applications increases, growing concerns about data privacy expose the limitations of traditional cloud-based machine learning methods that rely on centralized data collection and processing. Federated learning emerges as a promising alternative, offering a novel approach to training machine learning models that safeguards data privacy. Federated learning facilitates collaborative model training across various entities. In this approach, each user trains models locally and shares only the local model parameters with a central server, which then generates a global model based on these individual updates. This approach ensures data privacy since the training data itself is never directly shared with a central entity. However, existing federated machine learning frameworks are not without challenges. In terms of server design, these frameworks exhibit limited scalability with an increasing number of clients and are highly vulnerable to system faults, particularly as the central server becomes a single point of failure. This paper introduces Micro-FL, a federated learning framework that uses a microservices architecture to implement the federated learning system. It demonstrates that the framework is fault-tolerant and scalable, showing its ability to handle an increasing number of clients. A comprehensive performance evaluation confirms that Micro-FL proficiently handles component faults, enabling a smooth and uninterrupted operation.","PeriodicalId":509567,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140438557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Systematic Survey on 5G and 6G Security Considerations, Challenges, Trends, and Research Areas 关于 5G 和 6G 安全考虑因素、挑战、趋势和研究领域的系统调查

Future Internet Pub Date : 2024-02-20 DOI: 10.3390/fi16030067

Paul Scalise, Matthew Boeding, M. Hempel, H. Sharif, Joseph Delloiacovo, John Reed

{"title":"A Systematic Survey on 5G and 6G Security Considerations, Challenges, Trends, and Research Areas","authors":"Paul Scalise, Matthew Boeding, M. Hempel, H. Sharif, Joseph Delloiacovo, John Reed","doi":"10.3390/fi16030067","DOIUrl":"https://doi.org/10.3390/fi16030067","url":null,"abstract":"With the rapid rollout and growing adoption of 3GPP 5thGeneration (5G) cellular services, including in critical infrastructure sectors, it is important to review security mechanisms, risks, and potential vulnerabilities within this vital technology. Numerous security capabilities need to work together to ensure and maintain a sufficiently secure 5G environment that places user privacy and security at the forefront. Confidentiality, integrity, and availability are all pillars of a privacy and security framework that define major aspects of 5G operations. They are incorporated and considered in the design of the 5G standard by the 3rd Generation Partnership Project (3GPP) with the goal of providing a highly reliable network operation for all. Through a comprehensive review, we aim to analyze the ever-evolving landscape of 5G, including any potential attack vectors and proposed measures to mitigate or prevent these threats. This paper presents a comprehensive survey of the state-of-the-art research that has been conducted in recent years regarding 5G systems, focusing on the main components in a systematic approach: the Core Network (CN), Radio Access Network (RAN), and User Equipment (UE). Additionally, we investigate the utilization of 5G in time-dependent, ultra-confidential, and private communications built around a Zero Trust approach. In today’s world, where everything is more connected than ever, Zero Trust policies and architectures can be highly valuable in operations containing sensitive data. Realizing a Zero Trust Architecture entails continuous verification of all devices, users, and requests, regardless of their location within the network, and grants permission only to authorized entities. Finally, developments and proposed methods of new 5G and future 6G security approaches, such as Blockchain technology, post-quantum cryptography (PQC), and Artificial Intelligence (AI) schemes, are also discussed to understand better the full landscape of current and future research within this telecommunications domain.","PeriodicalId":509567,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140448075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT 物联网中医疗保健数据聚合的高能效重复数据删除机制

Future Internet Pub Date : 2024-02-19 DOI: 10.3390/fi16020066

Muhammad Nafees Ulfat Khan, Weiping Cao, Zhiling Tang, A. Ullah, Wanghua Pan

{"title":"Energy-Efficient De-Duplication Mechanism for Healthcare Data Aggregation in IoT","authors":"Muhammad Nafees Ulfat Khan, Weiping Cao, Zhiling Tang, A. Ullah, Wanghua Pan","doi":"10.3390/fi16020066","DOIUrl":"https://doi.org/10.3390/fi16020066","url":null,"abstract":"The rapid development of the Internet of Things (IoT) has opened the way for transformative advances in numerous fields, including healthcare. IoT-based healthcare systems provide unprecedented opportunities to gather patients’ real-time data and make appropriate decisions at the right time. Yet, the deployed sensors generate normal readings most of the time, which are transmitted to Cluster Heads (CHs). Handling these voluminous duplicated data is quite challenging. The existing techniques have high energy consumption, storage costs, and communication costs. To overcome these problems, in this paper, an innovative Energy-Efficient Fuzzy Data Aggregation System (EE-FDAS) has been presented. In it, at the first level, it is checked that sensors either generate normal or critical readings. In the first case, readings are converted to Boolean digit 0. This reduced data size takes only 1 digit which considerably reduces energy consumption. In the second scenario, sensors generating irregular readings are transmitted in their original 16 or 32-bit form. Then, data are aggregated and transmitted to respective CHs. Afterwards, these data are further transmitted to Fog servers, from where doctors have access. Lastly, for later usage, data are stored in the cloud server. For checking the proficiency of the proposed EE-FDAS scheme, extensive simulations are performed using NS-2.35. The results showed that EE-FDAS has performed well in terms of aggregation factor, energy consumption, packet drop rate, communication, and storage cost.","PeriodicalId":509567,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140451323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IoTwins: Implementing Distributed and Hybrid Digital Twins in Industrial Manufacturing and Facility Management Settings IoTwins：在工业制造和设施管理环境中实施分布式和混合式数字孪生系统

Future Internet Pub Date : 2024-02-17 DOI: 10.3390/fi16020065

Paolo Bellavista, G. Modica

{"title":"IoTwins: Implementing Distributed and Hybrid Digital Twins in Industrial Manufacturing and Facility Management Settings","authors":"Paolo Bellavista, G. Modica","doi":"10.3390/fi16020065","DOIUrl":"https://doi.org/10.3390/fi16020065","url":null,"abstract":"A Digital Twin (DT) refers to a virtual representation or digital replica of a physical object, system, process, or entity. This concept involves creating a detailed, real-time digital counterpart that mimics the behavior, characteristics, and attributes of its physical counterpart. DTs have the potential to improve efficiency, reduce costs, and enhance decision-making by providing a detailed, real-time understanding of the physical systems they represent. While this technology is finding application in numerous fields, such as energy, healthcare, and transportation, it appears to be a key component of the digital transformation of industries fostered by the fourth Industrial revolution (Industry 4.0). In this paper, we present the research results achieved by IoTwins, a European research project aimed at investigating opportunities and issues of adopting DTs in the fields of industrial manufacturing and facility management. Particularly, we discuss a DT model and a reference architecture for use by the research community to implement a platform for the development and deployment of industrial DTs in the cloud continuum. Guided by the devised architectures’ principles, we implemented an open platform and a development methodology to help companies build DT-based industrial applications and deploy them in the so-called Edge/Cloud continuum. To prove the research value and the usability of the implemented platform, we discuss a simple yet practical development use case.","PeriodicalId":509567,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140453394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Merging Ontologies and Data from Electronic Health Records 合并电子健康记录中的本体和数据

Future Internet Pub Date : 2024-02-17 DOI: 10.3390/fi16020062

Salvatore Calcagno, Andrea Calvagna, E. Tramontana, Gabriella Verga

{"title":"Merging Ontologies and Data from Electronic Health Records","authors":"Salvatore Calcagno, Andrea Calvagna, E. Tramontana, Gabriella Verga","doi":"10.3390/fi16020062","DOIUrl":"https://doi.org/10.3390/fi16020062","url":null,"abstract":"The Electronic Health Record (EHR) is a system for collecting and storing patient medical records as data that can be mechanically accessed, hence facilitating and assisting the medical decision-making process. EHRs exist in several formats, and each format lists thousands of keywords to classify patients data. The keywords are specific and are medical jargon; hence, data classification is very accurate. As the keywords constituting the formats of medical records express concepts by means of specific jargon without definitions or references, their proper use is left to clinicians and could be affected by their background, hence the interpretation of data could become slow or less accurate than that desired. This article presents an approach that accurately relates data in EHRs to ontologies in the medical realm. Thanks to ontologies, clinicians can be assisted when writing or analysing health records, e.g., our solution promptly suggests rigorous definitions for scientific terms, and automatically connects data spread over several parts of EHRs. The first step of our approach consists of converting selected data and keywords from several EHR formats into a format easier to parse, then the second step is merging the extracted data with specialised medical ontologies. Finally, enriched versions of the medical data are made available to professionals. The proposed approach was validated by taking samples of medical records and ontologies in the real world. The results have shown both versatility on handling data, precision of query results, and appropriate suggestions for relations among medical records.","PeriodicalId":509567,"journal":{"name":"Future Internet","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140453731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0