288 research outputs found
Multidisciplinary perspectives on Artificial Intelligence and the law
This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio
NEMISA Digital Skills Conference (Colloquium) 2023
The purpose of the colloquium and events centred around the central role that data plays
today as a desirable commodity that must become an important part of massifying digital
skilling efforts. Governments amass even more critical data that, if leveraged, could
change the way public services are delivered, and even change the social and economic
fortunes of any country. Therefore, smart governments and organisations increasingly
require data skills to gain insights and foresight, to secure themselves, and for improved
decision making and efficiency. However, data skills are scarce, and even more
challenging is the inconsistency of the associated training programs with most curated for
the Science, Technology, Engineering, and Mathematics (STEM) disciplines.
Nonetheless, the interdisciplinary yet agnostic nature of data means that there is
opportunity to expand data skills into the non-STEM disciplines as well.College of Engineering, Science and Technolog
Cybersecurity applications of Blockchain technologies
With the increase in connectivity, the popularization of cloud services, and the rise
of the Internet of Things (IoT), decentralized approaches for trust management
are gaining momentum. Since blockchain technologies provide a distributed ledger,
they are receiving massive attention from the research community in different application
fields. However, this technology does not provide cybersecurity by itself.
Thus, this thesis first aims to provide a comprehensive review of techniques and
elements that have been proposed to achieve cybersecurity in blockchain-based systems.
The analysis is intended to target area researchers, cybersecurity specialists
and blockchain developers. We present a series of lessons learned as well. One of
them is the rise of Ethereum as one of the most used technologies.
Furthermore, some intrinsic characteristics of the blockchain, like permanent
availability and immutability made it interesting for other ends, namely as covert
channels and malicious purposes.
On the one hand, the use of blockchains by malwares has not been characterized
yet. Therefore, this thesis also analyzes the current state of the art in this area. One
of the lessons learned is that covert communications have received little attention.
On the other hand, although previous works have analyzed the feasibility of
covert channels in a particular blockchain technology called Bitcoin, no previous
work has explored the use of Ethereum to establish a covert channel considering all
transaction fields and smart contracts.
To foster further defence-oriented research, two novel mechanisms are presented
on this thesis. First, Zephyrus takes advantage of all Ethereum fields and smartcontract
bytecode. Second, Smart-Zephyrus is built to complement Zephyrus by
leveraging smart contracts written in Solidity. We also assess the mechanisms feasibility
and cost. Our experiments show that Zephyrus, in the best case, can embed
40 Kbits in 0.57 s. for US 1.82 per bit), the provided stealthiness might be worth the price for attackers. Furthermore,
these two mechanisms can be combined to increase capacity and reduce
costs.Debido al aumento de la conectividad, la popularización de los servicios en la nube
y el auge del Internet de las cosas (IoT), los enfoques descentralizados para la
gestión de la confianza están cobrando impulso. Dado que las tecnologías de cadena
de bloques (blockchain) proporcionan un archivo distribuido, están recibiendo
una atención masiva por parte de la comunidad investigadora en diferentes campos
de aplicación. Sin embargo, esta tecnología no proporciona ciberseguridad por sí
misma. Por lo tanto, esta tesis tiene como primer objetivo proporcionar una revisión
exhaustiva de las técnicas y elementos que se han propuesto para lograr la ciberseguridad
en los sistemas basados en blockchain. Este análisis está dirigido a investigadores
del área, especialistas en ciberseguridad y desarrolladores de blockchain. A
su vez, se presentan una serie de lecciones aprendidas, siendo una de ellas el auge
de Ethereum como una de las tecnologías más utilizadas.
Asimismo, algunas características intrínsecas de la blockchain, como la disponibilidad
permanente y la inmutabilidad, la hacen interesante para otros fines, concretamente
como canal encubierto y con fines maliciosos.
Por una parte, aún no se ha caracterizado el uso de la blockchain por parte
de malwares. Por ello, esta tesis también analiza el actual estado del arte en este
ámbito. Una de las lecciones aprendidas al analizar los datos es que las comunicaciones
encubiertas han recibido poca atención.
Por otro lado, aunque trabajos anteriores han analizado la viabilidad de los
canales encubiertos en una tecnología blockchain concreta llamada Bitcoin, ningún
trabajo anterior ha explorado el uso de Ethereum para establecer un canal encubierto
considerando todos los campos de transacción y contratos inteligentes.
Con el objetivo de fomentar una mayor investigación orientada a la defensa,
en esta tesis se presentan dos mecanismos novedosos. En primer lugar, Zephyrus
aprovecha todos los campos de Ethereum y el bytecode de los contratos inteligentes.
En segundo lugar, Smart-Zephyrus complementa Zephyrus aprovechando los contratos inteligentes escritos en Solidity. Se evalúa, también, la viabilidad y el coste
de ambos mecanismos. Los resultados muestran que Zephyrus, en el mejor de los
casos, puede ocultar 40 Kbits en 0,57 s. por 1,64 US$, y recuperarlos en 2,8 s.
Smart-Zephyrus, por su parte, es capaz de ocultar un secreto de 4 Kb en 41 s. Si
bien es cierto que es caro (alrededor de 1,82 dólares por bit), el sigilo proporcionado
podría valer la pena para los atacantes. Además, estos dos mecanismos pueden
combinarse para aumentar la capacidad y reducir los costesPrograma de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: José Manuel Estévez Tapiador.- Secretario: Jorge Blasco Alís.- Vocal: Luis Hernández Encina
Security considerations in the open source software ecosystem
Open source software plays an important role in the software supply chain, allowing stakeholders to
utilize open source components as building blocks in their software, tooling, and infrastructure. But
relying on the open source ecosystem introduces unique challenges, both in terms of security and trust,
as well as in terms of supply chain reliability.
In this dissertation, I investigate approaches, considerations, and encountered challenges of stakeholders in the context of security, privacy, and trustworthiness of the open source software supply
chain. Overall, my research aims to empower and support software experts with the knowledge and
resources necessary to achieve a more secure and trustworthy open source software ecosystem. In the
first part of this dissertation, I describe a research study investigating the security and trust practices
in open source projects by interviewing 27 owners, maintainers, and contributors from a diverse set
of projects to explore their behind-the-scenes processes, guidance and policies, incident handling, and
encountered challenges, finding that participants’ projects are highly diverse in terms of their deployed
security measures and trust processes, as well as their underlying motivations. More on the consumer
side of the open source software supply chain, I investigated the use of open source components in
industry projects by interviewing 25 software developers, architects, and engineers to understand their
projects’ processes, decisions, and considerations in the context of external open source code, finding
that open source components play an important role in many of the industry projects, and that most
projects have some form of company policy or best practice for including external code. On the side of
end-user focused software, I present a study investigating the use of software obfuscation in Android
applications, which is a recommended practice to protect against plagiarism and repackaging. The
study leveraged a multi-pronged approach including a large-scale measurement, a developer survey, and
a programming experiment, finding that only 24.92% of apps are obfuscated by their developer, that
developers do not fear theft of their own apps, and have difficulties obfuscating their own apps. Lastly,
to involve end users themselves, I describe a survey with 200 users of cloud office suites to investigate
their security and privacy perceptions and expectations, with findings suggesting that users are generally
aware of basic security implications, but lack technical knowledge for envisioning some threat models.
The key findings of this dissertation include that open source projects have highly diverse security
measures, trust processes, and underlying motivations. That the projects’ security and trust needs are
likely best met in ways that consider their individual strengths, limitations, and project stage, especially
for smaller projects with limited access to resources. That open source components play an important
role in industry projects, and that those projects often have some form of company policy or best
practice for including external code, but developers wish for more resources to better audit included
components.
This dissertation emphasizes the importance of collaboration and shared responsibility in building and maintaining the open source software ecosystem, with developers, maintainers, end users,
researchers, and other stakeholders alike ensuring that the ecosystem remains a secure, trustworthy, and
healthy resource for everyone to rely on
Timely Classification of Encrypted or ProtocolObfuscated Internet Traffic Using Statistical Methods
Internet traffic classification aims to identify the type of application or protocol that generated
a particular packet or stream of packets on the network. Through traffic classification,
Internet Service Providers (ISPs), governments, and network administrators can
access basic functions and several solutions, including network management, advanced
network monitoring, network auditing, and anomaly detection. Traffic classification is
essential as it ensures the Quality of Service (QoS) of the network, as well as allowing
efficient resource planning.
With the increase of encrypted or obfuscated protocol traffic on the Internet and multilayer
data encapsulation, some classical classification methods have lost interest from the
scientific community. The limitations of traditional classification methods based on port
numbers and payload inspection to classify encrypted or obfuscated Internet traffic have
led to significant research efforts focused on Machine Learning (ML) based classification
approaches using statistical features from the transport layer. In an attempt to increase
classification performance, Machine Learning strategies have gained interest from the scientific
community and have shown promise in the future of traffic classification, specially
to recognize encrypted traffic.
However, ML approach also has its own limitations, as some of these methods have a
high computational resource consumption, which limits their application when classifying
large traffic or realtime
flows. Limitations of ML application have led to the investigation
of alternative approaches, including featurebased
procedures and statistical methods. In
this sense, statistical analysis methods, such as distances and divergences, have been used
to classify traffic in large flows and in realtime.
The main objective of statistical distance is to differentiate flows and find a pattern in
traffic characteristics through statistical properties, which enable classification. Divergences
are functional expressions often related to information theory, which measure the
degree of discrepancy between any two distributions.
This thesis focuses on proposing a new methodological approach to classify encrypted
or obfuscated Internet traffic based on statistical methods that enable the evaluation of
network traffic classification performance, including the use of computational resources
in terms of CPU and memory. A set of traffic classifiers based on KullbackLeibler
and
JensenShannon
divergences, and Euclidean, Hellinger, Bhattacharyya, and Wootters distances
were proposed. The following are the four main contributions to the advancement
of scientific knowledge reported in this thesis.
First, an extensive literature review on the classification of encrypted and obfuscated Internet traffic was conducted. The results suggest that portbased
and payloadbased
methods are becoming obsolete due to the increasing use of traffic encryption and multilayer
data encapsulation. MLbased
methods are also becoming limited due to their computational
complexity. As an alternative, Support Vector Machine (SVM), which is also
an ML method, and the KolmogorovSmirnov
and Chisquared
tests can be used as reference
for statistical classification. In parallel, the possibility of using statistical methods
for Internet traffic classification has emerged in the literature, with the potential of good
results in classification without the need of large computational resources. The potential
statistical methods are Euclidean Distance, Hellinger Distance, Bhattacharyya Distance,
Wootters Distance, as well as KullbackLeibler
(KL) and JensenShannon
divergences.
Second, we present a proposal and implementation of a classifier based on SVM for P2P
multimedia traffic, comparing the results with KolmogorovSmirnov
(KS) and Chisquare
tests. The results suggest that SVM classification with Linear kernel leads to a better classification
performance than KS and Chisquare
tests, depending on the value assigned to
the Self C parameter. The SVM method with Linear kernel and suitable values for the Self
C parameter may be a good choice to identify encrypted P2P multimedia traffic on the
Internet.
Third, we present a proposal and implementation of two classifiers based on KL Divergence
and Euclidean Distance, which are compared to SVM with Linear kernel, configured
with the standard Self C parameter, showing a reduced ability to classify flows based
solely on packet sizes compared to KL and Euclidean Distance methods. KL and Euclidean
methods were able to classify all tested applications, particularly streaming and P2P,
where for almost all cases they efficiently identified them with high accuracy, with reduced
consumption of computational resources. Based on the obtained results, it can be
concluded that KL and Euclidean Distance methods are an alternative to SVM, as these
statistical approaches can operate in realtime
and do not require retraining every time a
new type of traffic emerges.
Fourth, we present a proposal and implementation of a set of classifiers for encrypted
Internet traffic, based on JensenShannon
Divergence and Hellinger, Bhattacharyya, and
Wootters Distances, with their respective results compared to those obtained with methods
based on Euclidean Distance, KL, KS, and ChiSquare.
Additionally, we present a comparative
qualitative analysis of the tested methods based on Kappa values and Receiver
Operating Characteristic (ROC) curves. The results suggest average accuracy values above
90% for all statistical methods, classified as ”almost perfect reliability” in terms of Kappa
values, with the exception of KS. This result indicates that these methods are viable options
to classify encrypted Internet traffic, especially Hellinger Distance, which showed
the best Kappa values compared to other classifiers. We conclude that the considered
statistical methods can be accurate and costeffective
in terms of computational resource
consumption to classify network traffic. Our approach was based on the classification of Internet network traffic, focusing on statistical
distances and divergences. We have shown that it is possible to classify and obtain
good results with statistical methods, balancing classification performance and the
use of computational resources in terms of CPU and memory. The validation of the proposal
supports the argument of this thesis, which proposes the implementation of statistical
methods as a viable alternative to Internet traffic classification compared to methods
based on port numbers, payload inspection, and ML.A classificação de tráfego Internet visa identificar o tipo de aplicação ou protocolo que
gerou um determinado pacote ou fluxo de pacotes na rede. Através da classificação de
tráfego, Fornecedores de Serviços de Internet (ISP), governos e administradores de rede
podem ter acesso às funções básicas e várias soluções, incluindo gestão da rede, monitoramento
avançado de rede, auditoria de rede e deteção de anomalias. Classificar o tráfego é
essencial, pois assegura a Qualidade de Serviço (QoS) da rede, além de permitir planear
com eficiência o uso de recursos.
Com o aumento de tráfego cifrado ou protocolo ofuscado na Internet e do encapsulamento
de dados multicamadas, alguns métodos clássicos da classificação perderam interesse de
investigação da comunidade científica. As limitações dos métodos tradicionais da classificação
com base no número da porta e na inspeção de carga útil payload para classificar
o tráfego de Internet cifrado ou ofuscado levaram a esforços significativos de investigação
com foco em abordagens da classificação baseadas em técnicas de Aprendizagem
Automática (ML) usando recursos estatísticos da camada de transporte. Na tentativa
de aumentar o desempenho da classificação, as estratégias de Aprendizagem Automática
ganharam o interesse da comunidade científica e se mostraram promissoras no futuro da
classificação de tráfego, principalmente no reconhecimento de tráfego cifrado.
No entanto, a abordagem em ML também têm as suas próprias limitações,
pois alguns
desses métodos possuem um elevado consumo de recursos computacionais, o que limita
a sua aplicação para classificação de grandes fluxos de tráfego ou em tempo real. As limitações
no âmbito da aplicação de ML levaram à investigação de abordagens alternativas,
incluindo procedimentos baseados em características e métodos estatísticos. Neste sentido,
os métodos de análise estatística, tais como distâncias e divergências, têm sido utilizados
para classificar tráfego em grandes fluxos e em tempo real.
A distância estatística possui como objetivo principal diferenciar os fluxos e permite encontrar
um padrão nas características de tráfego através de propriedades estatísticas, que
possibilitam a classificação. As divergências são expressões funcionais frequentemente
relacionadas com a teoria da informação, que mede o grau de discrepância entre duas
distribuições quaisquer.
Esta tese focase
na proposta de uma nova abordagem metodológica para classificação de
tráfego cifrado ou ofuscado da Internet com base em métodos estatísticos que possibilite
avaliar o desempenho da classificação de tráfego de rede, incluindo a utilização de recursos
computacionais, em termos de CPU e memória. Foi proposto um conjunto de classificadores
de tráfego baseados nas Divergências de KullbackLeibler
e JensenShannon
e Distâncias Euclidiana, Hellinger, Bhattacharyya e Wootters. A seguir resumemse
os tese.
Primeiro, realizámos uma ampla revisão de literatura sobre classificação de tráfego cifrado
e ofuscado de Internet. Os resultados sugerem que os métodos baseados em porta e
baseados em carga útil estão se tornando obsoletos em função do crescimento da utilização
de cifragem de tráfego e encapsulamento de dados multicamada. O tipo de métodos
baseados em ML também está se tornando limitado em função da complexidade computacional.
Como alternativa, podese
utilizar a Máquina de Vetor de Suporte (SVM),
que também é um método de ML, e os testes de KolmogorovSmirnov
e Quiquadrado
como referência de comparação da classificação estatística. Em paralelo, surgiu na literatura
a possibilidade de utilização de métodos estatísticos para classificação de tráfego
de Internet, com potencial de bons resultados na classificação sem aporte de grandes recursos
computacionais. Os métodos estatísticos potenciais são as Distâncias Euclidiana,
Hellinger, Bhattacharyya e Wootters, além das Divergências de Kullback–Leibler (KL) e
JensenShannon.
Segundo, apresentamos uma proposta e implementação de um classificador baseado na
Máquina de Vetor de Suporte (SVM) para o tráfego multimédia P2P (PeertoPeer),
comparando
os resultados com os testes de KolmogorovSmirnov
(KS) e Quiquadrado.
Os
resultados sugerem que a classificação da SVM com kernel Linear conduz a um melhor
desempenho da classificação do que os testes KS e Quiquadrado,
dependente do valor
atribuído ao parâmetro Self C. O método SVM com kernel Linear e com valores adequados
para o parâmetro Self C pode ser uma boa escolha para identificar o tráfego Par a Par
(P2P) multimédia cifrado na Internet.
Terceiro, apresentamos uma proposta e implementação de dois classificadores baseados
na Divergência de KullbackLeibler (KL) e na Distância Euclidiana, sendo comparados
com a SVM com kernel Linear, configurado para o parâmestro Self C padrão, apresenta
reduzida
capacidade de classificar fluxos com base apenas nos tamanhos dos pacotes
em relação aos métodos KL e Distância Euclidiana. Os métodos KL e Euclidiano foram
capazes de classificar todas as aplicações testadas, destacandose
streaming e P2P, onde
para quase todos os casos foi eficiente identificálas
com alta precisão, com reduzido consumo
de recursos computacionais.Com base nos resultados obtidos, podese
concluir que
os métodos KL e Distância Euclidiana são uma alternativa à SVM, porque essas abordagens
estatísticas podem operar em tempo real e não precisam de retreinamento cada vez
que surge um novo tipo de tráfego.
Quarto, apresentamos uma proposta e implementação de um conjunto de classificadores
para o tráfego de Internet cifrado, baseados na Divergência de JensenShannon
e nas Distâncias
de Hellinger, Bhattacharyya e Wootters, sendo os respetivos resultados comparados
com os resultados obtidos com os métodos baseados na Distância Euclidiana, KL, KS e Quiquadrado.
Além disso, apresentamos uma análise qualitativa comparativa dos
métodos testados com base nos valores de Kappa e Curvas Característica de Operação do
Receptor (ROC). Os resultados sugerem valores médios de precisão acima de 90% para todos
os métodos estatísticos, classificados como “confiabilidade quase perfeita” em valores
de Kappa, com exceçãode KS. Esse resultado indica que esses métodos são opções viáveis
para a classificação de tráfego cifrado da Internet, em especial a Distância de Hellinger,
que apresentou os melhores resultados do valor de Kappa em comparaçãocom os demais
classificadores. Concluise
que os métodos estatísticos considerados podem ser precisos e
económicos em termos de consumo de recursos computacionais para classificar o tráfego
da rede.
A nossa abordagem baseouse
na classificação de tráfego de rede Internet, focando em
distâncias e divergências estatísticas. Nós mostramos que é possível classificar e obter
bons resultados com métodos estatísticos, equilibrando desempenho de classificação e
uso de recursos computacionais em termos de CPU e memória. A validação da proposta
sustenta o argumento desta tese, que propõe a implementação de métodos estatísticos
como alternativa viável à classificação de tráfego da Internet em relação aos métodos com
base no número da porta, na inspeção de carga útil e de ML.Thesis prepared at Instituto de Telecomunicações Delegação
da Covilhã and at the Department
of Computer Science of the University of Beira Interior, and submitted to the
University of Beira Interior for discussion in public session to obtain the Ph.D. Degree in
Computer Science and Engineering.
This work has been funded by Portuguese FCT/MCTES through national funds and, when
applicable, cofunded
by EU funds under the project UIDB/50008/2020, and by operation
Centro010145FEDER000019
C4
Centro
de Competências em Cloud Computing,
cofunded
by the European Regional Development Fund (ERDF/FEDER) through
the Programa Operacional Regional do Centro (Centro 2020). This work has also been
funded by CAPES (Brazilian Federal Agency for Support and Evaluation of Graduate Education)
within the Ministry of Education of Brazil under a scholarship supported by the
International Cooperation Program CAPES/COFECUB Project
9090134/
2013 at the
University of Beira Interior
Low-Coders, No-Coders, and Citizen Developers in Demand: Examining Knowledge, Skills, and Abilities Through a Job Market Analysis
The emergence of low-code/no-code (LCNC) platform technologies and the resulting increase in citizen development programs are facilitating the democratization of the design, development, and deployment of digital solutions. Citizen developers, non-technical employees who leverage LCNC platforms, are at the heart of this trend. While many firms perceive LCNC and citizen development as a crucial component of their digital transformation strategy, little is known about the evolving roles in this field or the necessary knowledge, skills, and abilities (KSA). To address this knowledge gap, we processed 113,106 job postings published on Indeed.com. Our topic modeling methodology identified 34 KSA topics and classified them into the three domains platform, business, and technology. We contribute to research by empirically demonstrating which competencies are required to successfully work in the LCNC field. Our findings can guide individual professionals and organizations alike
Undergraduate and Graduate Course Descriptions, 2023 Spring
Wright State University undergraduate and graduate course descriptions from Spring 2023
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