5,402 research outputs found
Owl ontology quality assessment and optimization in the cybersecurity domain
The purpose of this dissertation is to assess the quality of ontologies in patterns perceived by cybersecurity context. A content analysis between ontologies indicated that there were more pronounced differences in OWL ontologies in the cybersecurity field. Results showed an increase of relevance from expressivity to variability. Additionally, no differences were found in strategies used in most of the incidents. The ontology background needs to be emphasized to understand the quality of the phenomena. In addition, ontologies are a means of representing an area of knowledge through their semantic structure. The search of information and integration of data from different origins provides a common base that guarantees the coherence of the data. This can be categorized and described in a normative way. The unification of information with the world that surrounds us allows to create synergies between entities and relationships. However, the area of cybersecurity is one of the real-world domains where knowledge is uncertain. It is therefore necessary to analyze the challenges of choosing the appropriate representation of un-structured information. Vulnerabilities are identified, but incident response is not an automatic mechanism for understanding and processing unstructured text found on the web.O objetivo desta dissertação foi avaliar a qualidade das ontologias, em padrões percebidos pelo contexto de cibersegurança. Uma análise de conteúdo entre ontologias indicou que havia diferenças mais pronunciadas por ontologias OWL no campo da cibersegurança. Os resultados mostram um aumento da relevância de expressividade para a variabilidade. Além disso, não foram encontradas diferenças em estratégias utilizadas na maioria dos incidentes. O conhecimento das ontologias precisa de ser enfatizado para se entender os fenómenos de qualidade. Além disso, as ontologias são um meio de representar uma área de conhecimento através da sua estrutura semântica e facilita a pesquisa de informações e a integração de dados de diferentes origens, pois fornecem uma base comum que garante a coerência dos dados, categorizados e descritos, de forma normativa. A unificação da informação com o mundo que nos rodeia permite criar sinergias entre entidades e relacionamentos. No entanto, a área de cibersegurança é um dos domínios do mundo real em que o conhecimento é incerto e é fundamental analisar os desafios de escolher a representação apropriada de informações não estruturadas. As vulnerabilidades são identificadas, mas a resposta a incidentes não é um mecanismo automático para se entender e processar textos não estruturados encontrados na web
An ontological modelling of multi-attribute criticality analysis to guide Prognostics and Health Management program development
Digital technologies are becoming more pervasive and industrial companies are exploiting them to enhance the potentialities related to Prognostics and Health Management (PHM). Indeed, PHM allows to evaluate the health state of the physical assets as well as to predict their future behaviour. To be effective in developing PHM programs, the most critical assets should be identified so to direct modelling efforts. Several techniques could be adopted to evaluate asset criticality; in industrial practice, criticality analysis is amongst the most utilised. Despite the advancement of artificial intelligence for data analysis and predictions, the criticality analysis, which is built upon both quantitative and qualitative data, has not been improved accordingly. It is the goal of this work to propose an ontological formalisation of a multi-attribute criticality analysis in order to i) fix the semantics behind the terms involved in the analysis, ii) standardize and uniform the way criticality analysis is performed, and iii) take advantage of the reasoning capabilities to automatically evaluate asset criticality and associate a suitable maintenance strategy. The developed ontology, called MOCA, is tested in a food company featuring a global footprint. The application shows that MOCA can accomplish the prefixed goals; specifically, high priority assets towards which direct PHM programs are identified. In the long run, ontologies could serve as a unique knowledge base that integrate multiple data and information across facilities in a consistent way. As such, they will enable advanced analytics to take place, allowing to move towards cognitive Cyber Physical Systems that enhance business performance for companies spread worldwide
Knowledge Representation in Engineering 4.0
This dissertation was developed in the context of the BMBF and EU/ECSEL funded
projects GENIAL! and Arrowhead Tools. In these projects the chair examines methods
of specifications and cooperations in the automotive value chain from OEM-Tier1-Tier2.
Goal of the projects is to improve communication and collaborative planning, especially
in early development stages. Besides SysML, the use of agreed vocabularies and on-
tologies for modeling requirements, overall context, variants, and many other items, is
targeted. This thesis proposes a web database, where data from the collaborative requirements elicitation is combined with an ontology-based approach that uses reasoning
capabilities.
For this purpose, state-of-the-art ontologies have been investigated and integrated that
entail domains like hardware/software, roadmapping, IoT, context, innovation and oth-
ers. New ontologies have been designed like a HW / SW allocation ontology and a
domain-specific "eFuse ontology" as well as some prototypes. The result is a modular
ontology suite and the GENIAL! Basic Ontology that allows us to model automotive
and microelectronic functions, components, properties and dependencies based on the
ISO26262 standard among these elements. Furthermore, context knowledge that influences design decisions such as future trends in legislation, society, environment, etc. is
included. These knowledge bases are integrated in a novel tool that allows for collabo-
rative innovation planning and requirements communication along the automotive value
chain. To start off the work of the project, an architecture and prototype tool was developed. Designing ontologies and knowing how to use them proved to be a non-trivial
task, requiring a lot of context and background knowledge. Some of this background
knowledge has been selected for presentation and was utilized either in designing models
or for later immersion. Examples are basic foundations like design guidelines for ontologies, ontology categories and a continuum of expressiveness of languages and advanced
content like multi-level theory, foundational ontologies and reasoning.
Finally, at the end, we demonstrate the overall framework, and show the ontology with
reasoning, database and APPEL/SysMD (AGILA ProPErty and Dependency Descrip-
tion Language / System MarkDown) and constraints of the hardware / software knowledge base. There, by example, we explore and solve roadmap constraints that are coupled
with a car model through a constraint solver.Diese Dissertation wurde im Kontext des von BMBF und EU / ECSEL gefördertem
Projektes GENIAL! und Arrowhead Tools entwickelt. In diesen Projekten untersucht der
Lehrstuhl Methoden zur Spezifikationen und Kooperation in der Automotive Wertschöp-
fungskette, von OEM zu Tier1 und Tier2. Ziel der Arbeit ist es die Kommunikation
und gemeinsame Planung, speziell in den frühen Entwicklungsphasen zu verbessern.
Neben SysML ist die Benutzung von vereinbarten Vokabularen und Ontologien in der
Modellierung von Requirements, des Gesamtkontextes, Varianten und vielen anderen
Elementen angezielt. Ontologien sind dabei eine Möglichkeit, um das Vermeiden von
Missverständnissen und Fehlplanungen zu unterstützen. Dieser Ansatz schlägt eine Web-
datenbank vor, wobei Ontologien das Teilen von Wissen und das logische Schlussfolgern
von implizitem Wissen und Regeln unterstützen.
Diese Arbeit beschreibt Ontologien für die Domäne des Engineering 4.0, oder spezifischer,
für die Domäne, die für das deutsche Projekt GENIAL! benötigt wurde. Dies betrifft
Domänen, wie Hardware und Software, Roadmapping, Kontext, Innovation, IoT und
andere. Neue Ontologien wurden entworfen, wie beispielsweise die Hardware-Software
Allokations-Ontologie und eine domänen-spezifische "eFuse Ontologie". Das Ergebnis war
eine modulare Ontologie-Bibliothek mit der GENIAL! Basic Ontology, die es erlaubt, automotive und mikroelektronische Komponenten, Funktionen, Eigenschaften und deren
Abhängigkeiten basierend auf dem ISO26262 Standard zu entwerfen. Des weiteren ist
Kontextwissen, welches Entwurfsentscheidungen beinflusst, inkludiert. Diese Wissensbasen sind in einem neuartigen Tool integriert, dass es ermöglicht, Roadmapwissen und
Anforderungen durch die Automobil- Wertschöpfungskette hinweg auszutauschen. On
tologien zu entwerfen und zu wissen, wie man diese benutzt, war dabei keine triviale
Aufgabe und benötigte viel Hintergrund- und Kontextwissen. Ausgewählte Grundlagen
hierfür sind Richtlinien, wie man Ontologien entwirft, Ontologiekategorien, sowie das
Spektrum an Sprachen und Formen von Wissensrepresentationen. Des weiteren sind fort-
geschrittene Methoden erläutert, z.B wie man mit Ontologien Schlußfolgerungen trifft.
Am Schluss wird das Overall Framework demonstriert, und die Ontologie mit Reason-
ing, Datenbank und APPEL/SysMD (AGILA ProPErty and Dependency Description
Language / System MarkDown) und Constraints der Hardware / Software Wissensbasis
gezeigt. Dabei werden exemplarisch Roadmap Constraints mit dem Automodell verbunden und durch den Constraint Solver gelöst und exploriert
Human-Intelligence and Machine-Intelligence Decision Governance Formal Ontology
Since the beginning of the human race, decision making and rational thinking played a pivotal role for mankind to either exist and succeed or fail and become extinct. Self-awareness, cognitive thinking, creativity, and emotional magnitude allowed us to advance civilization and to take further steps toward achieving previously unreachable goals. From the invention of wheels to rockets and telegraph to satellite, all technological ventures went through many upgrades and updates. Recently, increasing computer CPU power and memory capacity contributed to smarter and faster computing appliances that, in turn, have accelerated the integration into and use of artificial intelligence (AI) in organizational processes and everyday life. Artificial intelligence can now be found in a wide range of organizational systems including healthcare and medical diagnosis, automated stock trading, robotic production, telecommunications, space explorations, and homeland security. Self-driving cars and drones are just the latest extensions of AI. This thrust of AI into organizations and daily life rests on the AI community’s unstated assumption of its ability to completely replicate human learning and intelligence in AI. Unfortunately, even today the AI community is not close to completely coding and emulating human intelligence into machines. Despite the revolution of digital and technology in the applications level, there has been little to no research in addressing the question of decision making governance in human-intelligent and machine-intelligent (HI-MI) systems. There also exists no foundational, core reference, or domain ontologies for HI-MI decision governance systems. Further, in absence of an expert reference base or body of knowledge (BoK) integrated with an ontological framework, decision makers must rely on best practices or standards that differ from organization to organization and government to government, contributing to systems failure in complex mission critical situations. It is still debatable whether and when human or machine decision capacity should govern or when a joint human-intelligence and machine-intelligence (HI-MI) decision capacity is required in any given decision situation.
To address this deficiency, this research establishes a formal, top level foundational ontology of HI-MI decision governance in parallel with a grounded theory based body of knowledge which forms the theoretical foundation of a systemic HI-MI decision governance framework
Context Aware Computing for The Internet of Things: A Survey
As we are moving towards the Internet of Things (IoT), the number of sensors
deployed around the world is growing at a rapid pace. Market research has shown
a significant growth of sensor deployments over the past decade and has
predicted a significant increment of the growth rate in the future. These
sensors continuously generate enormous amounts of data. However, in order to
add value to raw sensor data we need to understand it. Collection, modelling,
reasoning, and distribution of context in relation to sensor data plays
critical role in this challenge. Context-aware computing has proven to be
successful in understanding sensor data. In this paper, we survey context
awareness from an IoT perspective. We present the necessary background by
introducing the IoT paradigm and context-aware fundamentals at the beginning.
Then we provide an in-depth analysis of context life cycle. We evaluate a
subset of projects (50) which represent the majority of research and commercial
solutions proposed in the field of context-aware computing conducted over the
last decade (2001-2011) based on our own taxonomy. Finally, based on our
evaluation, we highlight the lessons to be learnt from the past and some
possible directions for future research. The survey addresses a broad range of
techniques, methods, models, functionalities, systems, applications, and
middleware solutions related to context awareness and IoT. Our goal is not only
to analyse, compare and consolidate past research work but also to appreciate
their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201
Security Management Framework for the Internet of Things
The increase in the design and development of wireless communication technologies
offers multiple opportunities for the management and control of cyber-physical systems
with connections between smart and autonomous devices, which provide the delivery
of simplified data through the use of cloud computing. Given this relationship with the
Internet of Things (IoT), it established the concept of pervasive computing that allows
any object to communicate with services, sensors, people, and objects without human
intervention. However, the rapid growth of connectivity with smart applications through
autonomous systems connected to the internet has allowed the exposure of numerous
vulnerabilities in IoT systems by malicious users.
This dissertation developed a novel ontology-based cybersecurity framework to
improve security in IoT systems using an ontological analysis to adapt appropriate
security services addressed to threats. The composition of this proposal explores
two approaches: (1) design time, which offers a dynamic method to build security
services through the application of a methodology directed to models considering
existing business processes; and (2) execution time, which involves monitoring the IoT
environment, classifying vulnerabilities and threats, and acting in the environment,
ensuring the correct adaptation of existing services.
The validation approach was used to demonstrate the feasibility of implementing the
proposed cybersecurity framework. It implies the evaluation of the ontology to offer
a qualitative evaluation based on the analysis of several criteria and also a proof of
concept implemented and tested using specific industrial scenarios. This dissertation
has been verified by adopting a methodology that follows the acceptance in the research
community through technical validation in the application of the concept in an industrial
setting.O aumento no projeto e desenvolvimento de tecnologias de comunicação sem fio oferece
múltiplas oportunidades para a gestão e controle de sistemas ciber-físicos com conexões
entre dispositivos inteligentes e autônomos, os quais proporcionam a entrega de dados
simplificados através do uso da computação em nuvem. Diante dessa relação com
a Internet das Coisas (IoT) estabeleceu-se o conceito de computação pervasiva que
permite que qualquer objeto possa comunicar com os serviços, sensores, pessoas e objetos
sem intervenção humana. Entretanto, o rápido crescimento da conectividade com as
aplicações inteligentes através de sistemas autônomos conectados com a internet permitiu
a exposição de inúmeras vulnerabilidades dos sistemas IoT para usuários maliciosos.
Esta dissertação desenvolveu um novo framework de cibersegurança baseada em
ontologia para melhorar a segurança em sistemas IoT usando uma análise ontológica
para a adaptação de serviços de segurança apropriados endereçados para as ameaças. A
composição dessa proposta explora duas abordagens: (1) tempo de projeto, o qual oferece
um método dinâmico para construir serviços de segurança através da aplicação de uma
metodologia dirigida a modelos, considerando processos empresariais existentes; e (2)
tempo de execução, o qual envolve o monitoramento do ambiente IoT, a classificação de
vulnerabilidades e ameaças, e a atuação no ambiente garantindo a correta adaptação dos
serviços existentes.
Duas abordagens de validação foram utilizadas para demonstrar a viabilidade da
implementação do framework de cibersegurança proposto. Isto implica na avaliação da
ontologia para oferecer uma avaliação qualitativa baseada na análise de diversos critérios
e também uma prova de conceito implementada e testada usando cenários específicos.
Esta dissertação foi validada adotando uma metodologia que segue a validação na
comunidade científica através da validação técnica na aplicação do nosso conceito em
um cenário industrial
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