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RDF-based knowledge graphs require additional formalisms to be
fully context-aware, which is presented in this book. This book
also provides a collection of provenance techniques and
state-of-the-art metadata-enhanced, provenance-aware, knowledge
graph-based representations across multiple application domains, in
order to demonstrate how to combine graph-based data models and
provenance representations. This is important to make statements
authoritative, verifiable, and reproducible, such as in biomedical,
pharmaceutical, and cybersecurity applications, where the data
source and generator can be just as important as the data itself.
Capturing provenance is critical to ensure sound experimental
results and rigorously designed research studies for patient and
drug safety, pathology reports, and medical evidence generation.
Similarly, provenance is needed for cyberthreat intelligence
dashboards and attack maps that aggregate and/or fuse heterogeneous
data from disparate data sources to differentiate between
unimportant online events and dangerous cyberattacks, which is
demonstrated in this book. Without provenance, data reliability and
trustworthiness might be limited, causing data reuse, trust,
reproducibility and accountability issues. This book primarily
targets researchers who utilize knowledge graphs in their methods
and approaches (this includes researchers from a variety of
domains, such as cybersecurity, eHealth, data science, Semantic
Web, etc.). This book collects core facts for the state of the art
in provenance approaches and techniques, complemented by a critical
review of existing approaches. New research directions are also
provided that combine data science and knowledge graphs, for an
increasingly important research topic.
This book presents a collection of state-of-the-art approaches to
utilizing machine learning, formal knowledge bases and rule sets,
and semantic reasoning to detect attacks on communication networks,
including IoT infrastructures, to automate malicious code
detection, to efficiently predict cyberattacks in enterprises, to
identify malicious URLs and DGA-generated domain names, and to
improve the security of mHealth wearables. This book details how
analyzing the likelihood of vulnerability exploitation using
machine learning classifiers can offer an alternative to
traditional penetration testing solutions. In addition, the book
describes a range of techniques that support data aggregation and
data fusion to automate data-driven analytics in cyberthreat
intelligence, allowing complex and previously unknown cyberthreats
to be identified and classified, and countermeasures to be
incorporated in novel incident response and intrusion detection
mechanisms.
This book collects state-of-the-art curriculum development
considerations, training methods, techniques, and best practices,
as well as cybersecurity lab requirements and aspects to take into
account when setting up new labs, all based on hands-on experience
in teaching cybersecurity in higher education.In parallel with the
increasing number and impact of cyberattacks, there is a growing
demand for cybersecurity courses in higher education. More and more
educational institutions offer cybersecurity courses, which come
with unique and constantly evolving challenges not known in other
disciplines. For example, step-by-step guides may not work for some
of the students if the configuration of a computing environment is
not identical or similar enough to the one the workshop material is
based on, which can be a huge problem for blended and online
delivery modes. Using nested virtualization in a cloud
infrastructure might not be authentic for all kinds of exercises,
because some of its characteristics can be vastly different from an
enterprise network environment that would be the most important to
demonstrate to students. The availability of cybersecurity datasets
for training and educational purposes can be limited, and the
publicly available datasets might not suit a large share of
training materials, because they are often excessively documented,
but not only by authoritative websites, which render these
inappropriate for assignments and can be misleading for online
students following training workshops and looking for online
resources about datasets such as the Boss of the SOC (BOTS)
datasets. The constant changes of Kali Linux make it necessary to
regularly update training materials, because commands might not run
the same way they did a couple of months ago. The many challenges
of cybersecurity education are further complicated by the
continuous evolution of networking and cloud computing, hardware
and software, which shapes student expectations: what is acceptable
and respected today might be obsolete or even laughable tomorrow.
This book illustrates how to use description logic-based formalisms
to their full potential in the creation, indexing, and reuse of
multimedia semantics. To do so, it introduces researchers to
multimedia semantics by providing an in-depth review of
state-of-the-art standards, technologies, ontologies, and software
tools. It draws attention to the importance of formal grounding in
the knowledge representation of multimedia objects, the potential
of multimedia reasoning in intelligent multimedia applications, and
presents both theoretical discussions and best practices in
multimedia ontology engineering. Readers already familiar with
mathematical logic, Internet, and multimedia fundamentals will
learn to develop formally grounded multimedia ontologies, and map
concept definitions to high-level descriptors. The core reasoning
tasks, reasoning algorithms, and industry-leading reasoners are
presented, while scene interpretation via reasoning is also
demonstrated. Overall, this book offers readers an essential
introduction to the formal grounding of web ontologies, as well as
a comprehensive collection and review of description logics (DLs)
from the perspectives of expressivity and reasoning complexity. It
covers best practices for developing multimedia ontologies with
formal grounding to guarantee decidability and obtain the desired
level of expressivity while maximizing the reasoning potential. The
capabilities of such multimedia ontologies are demonstrated by DL
implementations with an emphasis on multimedia reasoning
applications.
This book presents a collection of state-of-the-art AI approaches
to cybersecurity and cyberthreat intelligence, offering strategic
defense mechanisms for malware, addressing cybercrime, and
assessing vulnerabilities to yield proactive rather than reactive
countermeasures. The current variety and scope of cybersecurity
threats far exceed the capabilities of even the most skilled
security professionals. In addition, analyzing yesterday's security
incidents no longer enables experts to predict and prevent
tomorrow's attacks, which necessitates approaches that go far
beyond identifying known threats. Nevertheless, there are promising
avenues: complex behavior matching can isolate threats based on the
actions taken, while machine learning can help detect anomalies,
prevent malware infections, discover signs of illicit activities,
and protect assets from hackers. In turn, knowledge representation
enables automated reasoning over network data, helping achieve
cybersituational awareness. Bringing together contributions by
high-caliber experts, this book suggests new research directions in
this critical and rapidly growing field.
RDF-based knowledge graphs require additional formalisms to be
fully context-aware, which is presented in this book. This book
also provides a collection of provenance techniques and
state-of-the-art metadata-enhanced, provenance-aware, knowledge
graph-based representations across multiple application domains, in
order to demonstrate how to combine graph-based data models and
provenance representations. This is important to make statements
authoritative, verifiable, and reproducible, such as in biomedical,
pharmaceutical, and cybersecurity applications, where the data
source and generator can be just as important as the data itself.
Capturing provenance is critical to ensure sound experimental
results and rigorously designed research studies for patient and
drug safety, pathology reports, and medical evidence generation.
Similarly, provenance is needed for cyberthreat intelligence
dashboards and attack maps that aggregate and/or fuse heterogeneous
data from disparate data sources to differentiate between
unimportant online events and dangerous cyberattacks, which is
demonstrated in this book. Without provenance, data reliability and
trustworthiness might be limited, causing data reuse, trust,
reproducibility and accountability issues. This book primarily
targets researchers who utilize knowledge graphs in their methods
and approaches (this includes researchers from a variety of
domains, such as cybersecurity, eHealth, data science, Semantic
Web, etc.). This book collects core facts for the state of the art
in provenance approaches and techniques, complemented by a critical
review of existing approaches. New research directions are also
provided that combine data science and knowledge graphs, for an
increasingly important research topic.
This book presents a collection of state-of-the-art approaches to
utilizing machine learning, formal knowledge bases and rule sets,
and semantic reasoning to detect attacks on communication networks,
including IoT infrastructures, to automate malicious code
detection, to efficiently predict cyberattacks in enterprises, to
identify malicious URLs and DGA-generated domain names, and to
improve the security of mHealth wearables. This book details how
analyzing the likelihood of vulnerability exploitation using
machine learning classifiers can offer an alternative to
traditional penetration testing solutions. In addition, the book
describes a range of techniques that support data aggregation and
data fusion to automate data-driven analytics in cyberthreat
intelligence, allowing complex and previously unknown cyberthreats
to be identified and classified, and countermeasures to be
incorporated in novel incident response and intrusion detection
mechanisms.
This book illustrates how to use description logic-based formalisms
to their full potential in the creation, indexing, and reuse of
multimedia semantics. To do so, it introduces researchers to
multimedia semantics by providing an in-depth review of
state-of-the-art standards, technologies, ontologies, and software
tools. It draws attention to the importance of formal grounding in
the knowledge representation of multimedia objects, the potential
of multimedia reasoning in intelligent multimedia applications, and
presents both theoretical discussions and best practices in
multimedia ontology engineering. Readers already familiar with
mathematical logic, Internet, and multimedia fundamentals will
learn to develop formally grounded multimedia ontologies, and map
concept definitions to high-level descriptors. The core reasoning
tasks, reasoning algorithms, and industry-leading reasoners are
presented, while scene interpretation via reasoning is also
demonstrated. Overall, this book offers readers an essential
introduction to the formal grounding of web ontologies, as well as
a comprehensive collection and review of description logics (DLs)
from the perspectives of expressivity and reasoning complexity. It
covers best practices for developing multimedia ontologies with
formal grounding to guarantee decidability and obtain the desired
level of expressivity while maximizing the reasoning potential. The
capabilities of such multimedia ontologies are demonstrated by DL
implementations with an emphasis on multimedia reasoning
applications.
This book presents a collection of state-of-the-art AI approaches
to cybersecurity and cyberthreat intelligence, offering strategic
defense mechanisms for malware, addressing cybercrime, and
assessing vulnerabilities to yield proactive rather than reactive
countermeasures. The current variety and scope of cybersecurity
threats far exceed the capabilities of even the most skilled
security professionals. In addition, analyzing yesterday's security
incidents no longer enables experts to predict and prevent
tomorrow's attacks, which necessitates approaches that go far
beyond identifying known threats. Nevertheless, there are promising
avenues: complex behavior matching can isolate threats based on the
actions taken, while machine learning can help detect anomalies,
prevent malware infections, discover signs of illicit activities,
and protect assets from hackers. In turn, knowledge representation
enables automated reasoning over network data, helping achieve
cybersituational awareness. Bringing together contributions by
high-caliber experts, this book suggests new research directions in
this critical and rapidly growing field.
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