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This book provides a broad overview of the key results and
frameworks for various NSAO tasks as well as discussing important
application areas. This book also covers neuro symbolic
reasoning frameworks such as LNN, LTN, and NeurASP and learning
frameworks. This would include differential inductive logic
programming, constraint learning and deep symbolic policy
learning. Additionally, application areas such a visual
question answering and natural language processing are discussed as
well as topics such as verification of neural networks and symbol
grounding. Detailed algorithmic descriptions, example logic
programs, and an online supplement that includes instructional
videos and slides provide thorough but concise coverage of this
important area of AI. Neuro symbolic artificial intelligence (NSAI)
encompasses the combination of deep neural networks with symbolic
logic for reasoning and learning tasks. NSAI frameworks are
now capable of embedding prior knowledge in deep learning
architectures, guiding the learning process with logical
constraints, providing symbolic explainability, and using
gradient-based approaches to learn logical statements.Â
Several approaches are seeing usage in various application
areas. This book is designed for researchers and
advanced-level students trying to understand the current landscape
of NSAI research as well as those looking to apply NSAI research in
areas such as natural language processing and visual question
answering. Practitioners who specialize in employing machine
learning and AI systems for operational use will find this book
useful as well.
This SpringerBrief discusses how to develop intelligent systems for
cyber attribution regarding cyber-attacks. Specifically, the
authors review the multiple facets of the cyber attribution problem
that make it difficult for "out-of-the-box" artificial intelligence
and machine learning techniques to handle. Attributing a
cyber-operation through the use of multiple pieces of technical
evidence (i.e., malware reverse-engineering and source tracking)
and conventional intelligence sources (i.e., human or signals
intelligence) is a difficult problem not only due to the effort
required to obtain evidence, but the ease with which an adversary
can plant false evidence. This SpringerBrief not only lays out the
theoretical foundations for how to handle the unique aspects of
cyber attribution - and how to update models used for this purpose
- but it also describes a series of empirical results, as well as
compares results of specially-designed frameworks for cyber
attribution to standard machine learning approaches. Cyber
attribution is not only a challenging problem, but there are also
problems in performing such research, particularly in obtaining
relevant data. This SpringerBrief describes how to use
capture-the-flag for such research, and describes issues from
organizing such data to running your own capture-the-flag
specifically designed for cyber attribution. Datasets and software
are also available on the companion website.
This book features a wide spectrum of the latest computer science
research relating to cyber warfare, including military and policy
dimensions. It is the first book to explore the scientific
foundation of cyber warfare and features research from the areas of
artificial intelligence, game theory, programming languages, graph
theory and more. The high-level approach and emphasis on scientific
rigor provides insights on ways to improve cyber warfare defense
worldwide. Cyber Warfare: Building the Scientific Foundation
targets researchers and practitioners working in cyber security,
especially government employees or contractors. Advanced-level
students in computer science and electrical engineering with an
interest in security will also find this content valuable as a
secondary textbook or reference.
This book features a wide spectrum of the latest computer science
research relating to cyber warfare, including military and policy
dimensions. It is the first book to explore the scientific
foundation of cyber warfare and features research from the areas of
artificial intelligence, game theory, programming languages, graph
theory and more. The high-level approach and emphasis on scientific
rigor provides insights on ways to improve cyber warfare defense
worldwide. Cyber Warfare: Building the Scientific Foundation
targets researchers and practitioners working in cyber security,
especially government employees or contractors. Advanced-level
students in computer science and electrical engineering with an
interest in security will also find this content valuable as a
secondary textbook or reference.
Imagine yourself as a military officer in a conflict zone trying to
identify locations of weapons caches supporting road-side bomb
attacks on your country's troops. Or imagine yourself as a public
health expert trying to identify the location of contaminated water
that is causing diarrheal diseases in a local population.
Geospatial abduction is a new technique introduced by the authors
that allows such problems to be solved. Geospatial Abduction
provides the mathematics underlying geospatial abduction and the
algorithms to solve them in practice; it has wide applicability and
can be used by practitioners and researchers in many different
fields. Real-world applications of geospatial abduction to military
problems are included. Compelling examples drawn from other domains
as diverse as criminology, epidemiology and archaeology are covered
as well. This book also includes access to a dedicated website on
geospatial abduction hosted by University of Maryland. Geospatial
Abduction targets practitioners working in general AI, game theory,
linear programming, data mining, machine learning, and more. Those
working in the fields of computer science, mathematics,
geoinformation, geological and biological science will also find
this book valuable.
Imagine yourself as a military officer in a conflict zone trying to
identify locations of weapons caches supporting road-side bomb
attacks on your country's troops. Or imagine yourself as a public
health expert trying to identify the location of contaminated water
that is causing diarrheal diseases in a local population.
Geospatial abduction is a new technique introduced by the authors
that allows such problems to be solved. Geospatial Abduction
provides the mathematics underlying geospatial abduction and the
algorithms to solve them in practice; it has wide applicability and
can be used by practitioners and researchers in many different
fields. Real-world applications of geospatial abduction to military
problems are included. Compelling examples drawn from other domains
as diverse as criminology, epidemiology and archaeology are covered
as well. This book also includes access to a dedicated website on
geospatial abduction hosted by University of Maryland. Geospatial
Abduction targets practitioners working in general AI, game theory,
linear programming, data mining, machine learning, and more. Those
working in the fields of computer science, mathematics,
geoinformation, geological and biological science will also find
this book valuable.
This book sheds light on the challenges facing social media in
combating malicious accounts, and aims to introduce current
practices to address the challenges. It further provides an
in-depth investigation regarding characteristics of "Pathogenic
Social Media (PSM),"by focusing on how they differ from other
social bots (e.g., trolls, sybils and cyborgs) and normal users as
well as how PSMs communicate to achieve their malicious goals. This
book leverages sophisticated data mining and machine learning
techniques for early identification of PSMs, using the relevant
information produced by these bad actors. It also presents
proactive intelligence with a multidisciplinary approach that
combines machine learning, data mining, causality analysis and
social network analysis, providing defenders with the ability to
detect these actors that are more likely to form malicious
campaigns and spread harmful disinformation. Over the past years,
social media has played a major role in massive dissemination of
misinformation online. Political events and public opinion on the
Web have been allegedly manipulated by several forms of accounts
including "Pathogenic Social Media (PSM)" accounts (e.g., ISIS
supporters and fake news writers). PSMs are key users in spreading
misinformation on social media - in viral proportions. Early
identification of PSMs is thus of utmost importance for social
media authorities in an effort toward stopping their propaganda.
The burden falls to automatic approaches that can identify these
accounts shortly after they began their harmful activities.
Researchers and advanced-level students studying and working in
cybersecurity, data mining, machine learning, social network
analysis and sociology will find this book useful. Practitioners of
proactive cyber threat intelligence and social media authorities
will also find this book interesting and insightful, as it presents
an important and emerging type of threat intelligence facing social
media and the general public.
Malicious hackers utilize the World Wide Web to share knowledge.
Analyzing the online communication of these threat actors can help
reduce the risk of attacks. This book shifts attention from the
defender environment to the attacker environment, offering a new
security paradigm of 'proactive cyber threat intelligence' that
allows defenders of computer networks to gain a better
understanding of their adversaries by analyzing assets,
capabilities, and interest of malicious hackers. The authors
propose models, techniques, and frameworks based on threat
intelligence mined from the heart of the underground cyber world:
the malicious hacker communities. They provide insights into the
hackers themselves and the groups they form dynamically in the act
of exchanging ideas and techniques, buying or selling malware, and
exploits. The book covers both methodology - a hybridization of
machine learning, artificial intelligence, and social network
analysis methods - and the resulting conclusions, detailing how a
deep understanding of malicious hacker communities can be the key
to designing better attack prediction systems.
The important and rapidly emerging new field known as 'cyber threat
intelligence' explores the paradigm that defenders of computer
networks gain a better understanding of their adversaries by
understanding what assets they have available for an attack. In
this book, a team of experts examines a new type of cyber threat
intelligence from the heart of the malicious hacking underworld -
the dark web. These highly secure sites have allowed anonymous
communities of malicious hackers to exchange ideas and techniques,
and to buy/sell malware and exploits. Aimed at both cybersecurity
practitioners and researchers, this book represents a first step
toward a better understanding of malicious hacking communities on
the dark web and what to do about them. The authors examine
real-world darkweb data through a combination of human and
automated techniques to gain insight into these communities,
describing both methodology and results.
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