811 research outputs found
Analytical validation of innovative magneto-inertial outcomes: a controlled environment study.
peer reviewe
Challenges and Limitation Analysis of an IoT-Dependent System for Deployment in Smart Healthcare Using Communication Standards Features
The use of IoT technology is rapidly increasing in healthcare development and smart
healthcare system for fitness programs, monitoring, data analysis, etc. To improve the efficiency
of monitoring, various studies have been conducted in this field to achieve improved precision.
The architecture proposed herein is based on IoT integrated with a cloud system in which power
absorption and accuracy are major concerns. We discuss and analyze development in this domain
to improve the performance of IoT systems related to health care. Standards of communication for
IoT data transmission and reception can help to understand the exact power absorption in different
devices to achieve improved performance for healthcare development. We also systematically analyze
the use of IoT in healthcare systems using cloud features, as well as the performance and limitations
of IoT in this field. Furthermore, we discuss the design of an IoT system for efficient monitoring of
various healthcare issues in elderly people and limitations of an existing system in terms of resources,
power absorption and security when implemented in different devices as per requirements. Blood
pressure and heartbeat monitoring in pregnant women are examples of high-intensity applications
of NB-IoT (narrowband IoT), technology that supports widespread communication with a very
low data cost and minimum processing complexity and battery lifespan. This article also focuses
on analysis of the performance of narrowband IoT in terms of delay and throughput using singleand
multinode approaches. We performed analysis using the message queuing telemetry transport
protocol (MQTTP), which was found to be efficient compared to the limited application protocol
(LAP) in sending information from sensors.Ministerio Español de Ciencia e Innovación under project
number PID2020-115570GB-C22 (DemocratAI::UGR)Cátedra de Empresa Tecnología para
las Personas (UGR-Fujitsu
Deteção de intrusões de rede baseada em anomalias
Dissertação de mestrado integrado em Eletrónica Industrial e ComputadoresAo longo dos últimos anos, a segurança de hardware e software tornou-se uma grande preocupação. À medida
que a complexidade dos sistemas aumenta, as suas vulnerabilidades a sofisticadas técnicas de ataque têm
proporcionalmente escalado. Frequentemente o problema reside na heterogenidade de dispositivos conectados ao
veículo, tornando difícil a convergência da monitorização de todos os protocolos num único produto de segurança.
Por esse motivo, o mercado requer ferramentas mais avançadas para a monitorizar ambientes críticos à vida
humana, tais como os nossos automóveis.
Considerando que existem várias formas de interagir com os sistemas de entretenimento do automóvel como
o Bluetooth, o Wi-fi ou CDs multimédia, a necessidade de auditar as suas interfaces tornou-se uma prioridade,
uma vez que elas representam um sério meio de aceeso à rede interna do carro. Atualmente, os mecanismos de
segurança de um carro focam-se na monitotização da rede CAN, deixando para trás as tecnologias referidas e não
contemplando os sistemas não críticos. Como exemplo disso, o Bluetooth traz desafios diferentes da rede CAN,
uma vez que interage diretamente com o utilizador e está exposto a ataques externos.
Uma abordagem alternativa para tornar o automóvel num sistema mais robusto é manter sob supervisão as
comunicações que com este são estabelecidas. Ao implementar uma detecção de intrusão baseada em anomalias,
esta dissertação visa analisar o protocolo Bluetooth no sentido de identificar interações anormais que possam
alertar para uma situação fora dos padrões de utilização. Em última análise, este produto de software embebido
incorpora uma grande margem de auto-aprendizagem, que é vital para enfrentar quaisquer ameaças desconhecidas
e aumentar os níveis de segurança globais. Ao longo deste documento, apresentamos o estudo do problema seguido
de uma metodologia alternativa que implementa um algoritmo baseado numa LSTM para prever a sequência de
comandos HCI correspondentes a tráfego Bluetooth normal. Os resultados mostram a forma como esta abordagem
pode impactar a deteção de intrusões nestes ambientes ao demonstrar uma grande capacidade para identificar padrões anómalos no conjunto de dados considerado.In the last few years, hardware and software security have become a major concern. As the systems’ complexity
increases, its vulnerabilities to several sophisticated attack techniques have escalated likewise. Quite often, the
problem lies in the heterogeneity of the devices connected to the vehicle, making it difficult to converge the monitoring
systems of all existing protocols into one security product. Thereby, the market requires more refined tools to monitor
life-risky environments such as personal vehicles.
Considering that there are several ways to interact with the car’s infotainment system, such as Wi-fi, Bluetooth,
or CD player, the need to audit these interfaces has become a priority as they represent a serious channel to reach
the internal car network. Nowadays, security in car networks focuses on CAN bus monitoring, leaving behind the
aforementioned technologies and not contemplating other non-critical systems. As an example of these concerns,
Bluetooth brings different challenges compared to CAN as it interacts directly with the user, being exposed to external
attacks.
An alternative approach to converting modern vehicles and their set of computers into more robust systems
is to keep track of established communications with them. By enforcing anomaly-based intrusion detection this
dissertation aims to analyze the Bluetooth protocol to identify abnormal user interactions that may alert for a non conforming pattern. Ultimately, such embedded software product incorporates a self-learning edge, which is vital to
face newly developed threats and increasing global security levels. Throughout this document, we present the study
case followed by an alternative methodology that implements an LSTM based algorithm to predict a sequence of
HCI commands corresponding to normal Bluetooth traffic. The results show how this approach can impact intrusion
detection in such environments by expressing a high capability of identifying abnormal patterns in the considered
data
Artificial Intelligence and International Conflict in Cyberspace
This edited volume explores how artificial intelligence (AI) is transforming international conflict in cyberspace. Over the past three decades, cyberspace developed into a crucial frontier and issue of international conflict. However, scholarly work on the relationship between AI and conflict in cyberspace has been produced along somewhat rigid disciplinary boundaries and an even more rigid sociotechnical divide – wherein technical and social scholarship are seldomly brought into a conversation. This is the first volume to address these themes through a comprehensive and cross-disciplinary approach. With the intent of exploring the question ‘what is at stake with the use of automation in international conflict in cyberspace through AI?’, the chapters in the volume focus on three broad themes, namely: (1) technical and operational, (2) strategic and geopolitical and (3) normative and legal. These also constitute the three parts in which the chapters of this volume are organised, although these thematic sections should not be considered as an analytical or a disciplinary demarcation
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