351 research outputs found

    Risk and threat mitigation techniques in internet of things (IoT) environments: a survey

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    Security in the Internet of Things (IoT) remains a predominant area of concern. Although several other surveys have been published on this topic in recent years, the broad spectrum that this area aims to cover, the rapid developments and the variety of concerns make it impossible to cover the topic adequately. This survey updates the state of the art covered in previous surveys and focuses on defences and mitigations against threats rather than on the threats alone, an area that is less extensively covered by other surveys. This survey has collated current research considering the dynamicity of the IoT environment, a topic missed in other surveys and warrants particular attention. To consider the IoT mobility, a life-cycle approach is adopted to the study of dynamic and mobile IoT environments and means of deploying defences against malicious actors aiming to compromise an IoT network and to evolve their attack laterally within it and from it. This survey takes a more comprehensive and detailed step by analysing a broad variety of methods for accomplishing each of the mitigation steps, presenting these uniquely by introducing a “defence-in-depth” approach that could significantly slow down the progress of an attack in the dynamic IoT environment. This survey sheds a light on leveraging redundancy as an inherent nature of multi-sensor IoT applications, to improve integrity and recovery. This study highlights the challenges of each mitigation step, emphasises novel perspectives, and reconnects the discussed mitigation steps to the ground principles they seek to implement

    Techno-Economic Assessment in Communications: New Challenges

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    This article shows a brief history of Techno-Economic Assessment (TEA) in Communications, a proposed redefinition of TEA as well as the new challenges derived from a dynamic context with cloud-native virtualized networks, the Helium Network & alike blockchain-based decentralized networks, the new network as a platform (NaaP) paradigm, carbon pricing, network sharing, and web3, metaverse and blockchain technologies. The authors formulate the research question and show the need to improve TEA models to integrate and manage all this increasing complexity. This paper also proposes the characteristics TEA models should have and their current degree of compliance for several use cases: 5G and beyond, software-defined wide area network (SD-WAN), secure access service edge (SASE), secure service edge (SSE), and cloud cybersecurity risk assessment. The authors also present TEA extensibility to request for proposals (RFP) processes and other industries, to conclude that there is an urgent need for agile and effective TEA in Comms that allows industrialization of agile decision-making for all market stakeholders to choose the optimal solution for any technology, scenario and use case.Comment: 18 pages, 1 figure, 2 table

    Adaptive Data-driven Optimization using Transfer Learning for Resilient, Energy-efficient, Resource-aware, and Secure Network Slicing in 5G-Advanced and 6G Wireless Systems

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    Title from PDF of title page, viewed January 31, 2023Dissertation advisor: Cory BeardVitaIncludes bibliographical references (pages 134-141)Dissertation (Ph.D)--Department of Computer Science and Electrical Engineering. University of Missouri--Kansas City, 20225G–Advanced is the next step in the evolution of the fifth–generation (5G) technology. It will introduce a new level of expanded capabilities beyond connections and enables a broader range of advanced applications and use cases. 5G–Advanced will support modern applications with greater mobility and high dependability. Artificial intelligence and Machine Learning will enhance network performance with spectral efficiency and energy savings enhancements. This research established a framework to optimally control and manage an appropriate selection of network slices for incoming requests from diverse applications and services in Beyond 5G networks. The developed DeepSlice model is used to optimize the network and individual slice load efficiency across isolated slices and manage slice lifecycle in case of failure. The DeepSlice framework can predict the unknown connections by utilizing the learning from a developed deep-learning neural network model. The research also addresses threats to the performance, availability, and robustness of B5G networks by proactively preventing and resolving threats. The study proposed a Secure5G framework for authentication, authorization, trust, and control for a network slicing architecture in 5G systems. The developed model prevents the 5G infrastructure from Distributed Denial of Service by analyzing incoming connections and learning from the developed model. The research demonstrates the preventive measure against volume attacks, flooding attacks, and masking (spoofing) attacks. This research builds the framework towards the zero trust objective (never trust, always verify, and verify continuously) that improves resilience. Another fundamental difficulty for wireless network systems is providing a desirable user experience in various network conditions, such as those with varying network loads and bandwidth fluctuations. Mobile Network Operators have long battled unforeseen network traffic events. This research proposed ADAPTIVE6G to tackle the network load estimation problem using knowledge-inspired Transfer Learning by utilizing radio network Key Performance Indicators from network slices to understand and learn network load estimation problems. These algorithms enable Mobile Network Operators to optimally coordinate their computational tasks in stochastic and time-varying network states. Energy efficiency is another significant KPI in tracking the sustainability of network slicing. Increasing traffic demands in 5G dramatically increase the energy consumption of mobile networks. This increase is unsustainable in terms of dollar cost and environmental impact. This research proposed an innovative ECO6G model to attain sustainability and energy efficiency. Research findings suggested that the developed model can reduce network energy costs without negatively impacting performance or end customer experience against the classical Machine Learning and Statistical driven models. The proposed model is validated against the industry-standardized energy efficiency definition, and operational expenditure savings are derived, showing significant cost savings to MNOs.Introduction -- A deep neural network framework towards a resilient, efficient, and secure network slicing in Beyond 5G Networks -- Adaptive resource management techniques for network slicing in Beyond 5G networks using transfer learning -- Energy and cost analysis for network slicing deployment in Beyond 5G networks -- Conclusion and future scop

    Securing the Internet of Things: A Study on Machine Learning-Based Solutions for IoT Security and Privacy Challenges

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    The Internet of Things (IoT) is a rapidly growing technology that connects and integrates billions of smart devices, generating vast volumes of data and impacting various aspects of daily life and industrial systems. However, the inherent characteristics of IoT devices, including limited battery life, universal connectivity, resource-constrained design, and mobility, make them highly vulnerable to cybersecurity attacks, which are increasing at an alarming rate. As a result, IoT security and privacy have gained significant research attention, with a particular focus on developing anomaly detection systems. In recent years, machine learning (ML) has made remarkable progress, evolving from a lab novelty to a powerful tool in critical applications. ML has been proposed as a promising solution for addressing IoT security and privacy challenges. In this article, we conducted a study of the existing security and privacy challenges in the IoT environment. Subsequently, we present the latest ML-based models and solutions to address these challenges, summarizing them in a table that highlights the key parameters of each proposed model. Additionally, we thoroughly studied available datasets related to IoT technology. Through this article, readers will gain a detailed understanding of IoT architecture, security attacks, and countermeasures using ML techniques, utilizing available datasets. We also discuss future research directions for ML-based IoT security and privacy. Our aim is to provide valuable insights into the current state of research in this field and contribute to the advancement of IoT security and privacy

    Exploring the confluence of IoT and metaverse: Future opportunities and challenges

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    The Internet of Things (IoT) and the metaverse are two rapidly evolving technologies that have the potential to shape the future of our digital world. IoT refers to the network of physical devices, vehicles, buildings, and other objects that are connected to the internet and capable of collecting and sharing data. The metaverse, on the other hand, is a virtual world where users can interact with each other and digital objects in real time. In this research paper, we aim to explore the intersection of the IoT and metaverse and the opportunities and challenges that arise from their convergence. We will examine how IoT devices can be integrated into the metaverse to create new and immersive experiences for users. We will also analyse the potential use cases and applications of this technology in various industries such as healthcare, education, and entertainment. Additionally, we will discuss the privacy, security, and ethical concerns that arise from the use of IoT devices in the metaverse. A survey is conducted through a combination of a literature review and a case study analysis. This review will provide insights into the potential impact of IoT and metaverse on society and inform the development of future technologies in this field

    Estudio comparativo de plataformas IoT para el control y monitoreo remoto de un equipo mecánico para el envío y recepción datos

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    El internet de las cosas es la tecnología que permite conectar dispositivos a internet y las plataformas IoT las que permiten a un usuario final el monitoreo y control remoto de dichos dispositivos. Debido a esto, se propone la utilización de una plataforma IoT para monitorear y controlar remotamente los equipos mecánicos de la PUCP, como el banco de psicrometría ubicado en el Laboratorio de energía de la misma, para el desarrollo de laboratorios remotos. Por este motivo, el objetivo principal será el análisis y comparación de tres plataformas IoT que permitan el monitoreo y control remoto de un equipo mecánico: ThingSpeak, ThingsBoard y Kaa IoT. En la investigación, se evidencia que la plataforma que cumple con la mayoría de los requerimientos planteados para el desarrollo de un laboratorio remoto es Kaa IoT.Trabajo de investigació

    ОГЛЯД РІШЕНЬ З АПАРАТНОЇ БЕЗПЕКИ КІНЦЕВИХ ПРИСТРОЇВ ТУМАННИХ ОБЧИСЛЕНЬ У ІНТЕРНЕТІ РЕЧЕЙ

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    Предметом дослідження є можливі засоби підвищення апаратної безпеки кінцевих пристроїв туманних обчислень в мережах Інтернету речей (ІоТ), популярність якого щороку стрімко зростає та потребує високого рівня захищеності від усіх типів атак. Метою роботи є огляд доступних готових комерційних продуктів та/або концептуальних апаратних рішень для захисту бюджетних пристроїв у мережах Інтернету речей на основі туманних технологій. Для досягнення поставленої мети виконано такі завдання: запропоновано концепцію туманних обчислень та визначено переваги, які вона надає мережам IoT; розглянуто кіберзагрози та апаратні атаки на мережі ІоТ; описано наслідки використання мереж Інтернету речей на основі туманних обчислень; розглянуто апаратні засоби безпеки, такі як TRM, PUF, HSM тощо. Для вирішення завдань використано такі методи дослідження, як: теоретичний аналіз літературних джерел; порівняльний аналіз хмарних, туманних і мобільних обчислень; аналіз наявних апаратних засобів безпеки. Здобуто такі результати: туманні обчислення можна розглядати як шлюз між хмарними обчисленнями та Інтернетом речей; вони мають більшість із переваг хмарних обчислень, крім того, додатково дають змогу обробляти дані на кінцевих пристроях, не навантажуючи центральний сервер. Висновки: безпека апаратного забезпечення в системах Інтернету речей не менш важлива, ніж програмна безпека. Особливо вагомо це питання постає для систем на основі туманних обчислень, де дані оброблятимуться на периферії, без передачі в хмару. Для підвищення рівня апаратної безпеки пристроїв туманних обчислень пропонується використовувати стандартні апаратні платформи безпеки, такі як: фізично неклоновані функції, апаратний модуль безпеки, система на кристалі тощо. Апаратні компоненти системи, що застосовують туманні обчислення, менш схильні до кібератак, зломів, вторгнень чи маніпуляцій

    Dynamic Distributed Monitoring for 6LoWPAN-based IoT Networks

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    Mission-criticalal Internet of Things (IoT)-based networks are increasingly employed in daily and industrial infrastructures. The resilience of such networks is crucial. Given IoT networks’ constantly changing nature, it is necessary to provide dependability and sustainability. A robust network monitoring can reinforce reliability, such that the monitoring mechanism adapts itself to real-time network instabilities. This work proposes a proactive, dynamic, and distributed network monitoring mechanism with monitor placement and scheduling for 6LoWPAN-based IoT networks intended for mission-critical applications. The proposed mechanism aims to ensure real-time monitoring coverage while respecting the limited and changing power resources of devices to prolong the network lifetime
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