1,530 research outputs found
Cyber–Physical–Social Frameworks for Urban Big Data Systems: A Survey
The integration of things’ data on the Web and Web linking for things’ description and discovery is leading the way towards smart Cyber–Physical Systems (CPS). The data generated in CPS represents observations gathered by sensor devices about the ambient environment that can be manipulated by computational processes of the cyber world. Alongside this, the growing use of social networks offers near real-time citizen sensing capabilities as a complementary information source. The resulting Cyber–Physical–Social System (CPSS) can help to understand the real world and provide proactive services to users. The nature of CPSS data brings new requirements and challenges to different stages of data manipulation, including identification of data sources, processing and fusion of different types and scales of data. To gain an understanding of the existing methods and techniques which can be useful for a data-oriented CPSS implementation, this paper presents a survey of the existing research and commercial solutions. We define a conceptual framework for a data-oriented CPSS and detail the various solutions for building human–machine intelligence
AI-enabled modeling and monitoring of data-rich advanced manufacturing systems
The infrastructure of cyber-physical systems (CPS) is based on a meta-concept of cybermanufacturing systems (CMS) that synchronizes the Industrial Internet of Things (IIoTs), Cloud Computing, Industrial Control Systems (ICSs), and Big Data analytics in manufacturing operations. Artificial Intelligence (AI) can be incorporated to make intelligent decisions in the day-to-day operations of CMS. Cyberattack spaces in AI-based cybermanufacturing operations pose significant challenges, including unauthorized modification of systems, loss of historical data, destructive malware, software malfunctioning, etc. However, a cybersecurity framework can be implemented to prevent unauthorized access, theft, damage, or other harmful attacks on electronic equipment, networks, and sensitive data. The five main cybersecurity framework steps are divided into procedures and countermeasure efforts, including identifying, protecting, detecting, responding, and recovering. Given the major challenges in AI-enabled cybermanufacturing systems, three research objectives are proposed in this dissertation by incorporating cybersecurity frameworks. The first research aims to detect the in-situ additive manufacturing (AM) process authentication problem using high-volume video streaming data. A side-channel monitoring approach based on an in-situ optical imaging system is established, and a tensor-based layer-wise texture descriptor is constructed to describe the observed printing path. Subsequently, multilinear principal component analysis (MPCA) is leveraged to reduce the dimension of the tensor-based texture descriptor, and low-dimensional features can be extracted for detecting attack-induced alterations. The second research work seeks to address the high-volume data stream problems in multi-channel sensor fusion for diverse bearing fault diagnosis. This second approach proposes a new multi-channel sensor fusion method by integrating acoustics and vibration signals with different sampling rates and limited training data. The frequency-domain tensor is decomposed by MPCA, resulting in low-dimensional process features for diverse bearing fault diagnosis by incorporating a Neural Network classifier. By linking the second proposed method, the third research endeavor is aligned to recovery systems of multi-channel sensing signals when a substantial amount of missing data exists due to sensor malfunction or transmission issues. This study has leveraged a fully Bayesian CANDECOMP/PARAFAC (FBCP) factorization method that enables to capture of multi-linear interaction (channels × signals) among latent factors of sensor signals and imputes missing entries based on observed signals
Communication Theoretic Data Analytics
Widespread use of the Internet and social networks invokes the generation of
big data, which is proving to be useful in a number of applications. To deal
with explosively growing amounts of data, data analytics has emerged as a
critical technology related to computing, signal processing, and information
networking. In this paper, a formalism is considered in which data is modeled
as a generalized social network and communication theory and information theory
are thereby extended to data analytics. First, the creation of an equalizer to
optimize information transfer between two data variables is considered, and
financial data is used to demonstrate the advantages. Then, an information
coupling approach based on information geometry is applied for dimensionality
reduction, with a pattern recognition example to illustrate the effectiveness.
These initial trials suggest the potential of communication theoretic data
analytics for a wide range of applications.Comment: Published in IEEE Journal on Selected Areas in Communications, Jan.
201
Evaluating Resilience of Cyber-Physical-Social Systems
Nowadays, protecting the network is not the only security concern. Still, in cyber security,
websites and servers are becoming more popular as targets due to the ease with which
they can be accessed when compared to communication networks. Another threat in
cyber physical social systems with human interactions is that they can be attacked and
manipulated not only by technical hacking through networks, but also by manipulating
people and stealing users’ credentials. Therefore, systems should be evaluated beyond cy-
ber security, which means measuring their resilience as a piece of evidence that a system
works properly under cyber-attacks or incidents. In that way, cyber resilience is increas-
ingly discussed and described as the capacity of a system to maintain state awareness for
detecting cyber-attacks. All the tasks for making a system resilient should proactively
maintain a safe level of operational normalcy through rapid system reconfiguration to
detect attacks that would impact system performance. In this work, we broadly studied
a new paradigm of cyber physical social systems and defined a uniform definition of it.
To overcome the complexity of evaluating cyber resilience, especially in these inhomo-
geneous systems, we proposed a framework including applying Attack Tree refinements
and Hierarchical Timed Coloured Petri Nets to model intruder and defender behaviors
and evaluate the impact of each action on the behavior and performance of the system.Hoje em dia, proteger a rede não é a única preocupação de segurança. Ainda assim, na
segurança cibernética, sites e servidores estão se tornando mais populares como alvos
devido à facilidade com que podem ser acessados quando comparados às redes de comu-
nicação. Outra ameaça em sistemas sociais ciberfisicos com interações humanas é que eles
podem ser atacados e manipulados não apenas por hackers técnicos através de redes, mas
também pela manipulação de pessoas e roubo de credenciais de utilizadores. Portanto, os
sistemas devem ser avaliados para além da segurança cibernética, o que significa medir
sua resiliência como uma evidência de que um sistema funciona adequadamente sob
ataques ou incidentes cibernéticos. Dessa forma, a resiliência cibernética é cada vez mais
discutida e descrita como a capacidade de um sistema manter a consciência do estado para
detectar ataques cibernéticos. Todas as tarefas para tornar um sistema resiliente devem
manter proativamente um nível seguro de normalidade operacional por meio da reconfi-
guração rápida do sistema para detectar ataques que afetariam o desempenho do sistema.
Neste trabalho, um novo paradigma de sistemas sociais ciberfisicos é amplamente estu-
dado e uma definição uniforme é proposta. Para superar a complexidade de avaliar a
resiliência cibernética, especialmente nesses sistemas não homogéneos, é proposta uma
estrutura que inclui a aplicação de refinamentos de Árvores de Ataque e Redes de Petri
Coloridas Temporizadas Hierárquicas para modelar comportamentos de invasores e de-
fensores e avaliar o impacto de cada ação no comportamento e desempenho do sistema
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