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This book looks at industry change patterns and innovations (such
as artificial intelligence, machine learning, big data analysis,
and blockchain support and efficiency technology) that are speeding
up industrial transformation, industrial infrastructure,
biodiversity, and productivity. This book focuses on real-world
industrial applications and case studies to provide for a wider
knowledge of intelligent manufacturing. It also offers insights
into manufacturing, logistics, and supply chain, where systems have
undergone an industrial transformation. It discusses current
research of machine learning along with blockchain techniques that
can fill the gap between research and industrial exposure. It goes
on to cover the effects that the Fourth Industrial Revolution has
on industrial infrastructures and looks at the current industry
change patterns and innovations that are accelerating industrial
transformation activities. Researchers, scholars, and students from
different countries will appreciate this book for its real-world
applications and knowledge acquisition. This book targets
manufacturers, industry owners, product developers, scientists,
logistics, and supply chain engineers. Focuses on real-world
industrial applications and case studies to provide for a wider
knowledge of intelligent manufacturing Offers insights into
manufacturing, logistics, and supply chain where systems have
undergone an industrial transformation Discusses current research
of machine learning along with blockchain techniques that can fill
the gap between research and industrial exposure Covers the effects
that the 4th Industrial Revolution has on industrial
infrastructures Looks at industry change patterns and innovations
that are speeding up industrial transformation activities Om
Prakash Jena is currently working as an associate professor in the
Department of Computer Science, Ravenshaw University, Cuttack,
Odisha, India. Sabyasachi Pramanik is an assistant professor in the
Department of Computer Science and Engineering, Haldia Institute of
Technology, India. Ahmed A. Elngar is an associate professor in the
Faculty of Computers & Artificial Intelligence, Beni-Suef
University, Egypt. He is also an associate professor in the College
of Computer Information Technology, chair of the Scientific
Innovation Research Group (SIRG), and director of the Technological
and Informatics Studies Center (TISC), American University in the
Emirates, United Arab Emirates.
An Interdisciplinary Approach to Modern Network Security presents
the latest methodologies and trends in detecting and preventing
network threats. Investigating the potential of current and
emerging security technologies, this publication is an
all-inclusive reference source for academicians, researchers,
students, professionals, practitioners, network analysts and
technology specialists interested in the simulation and application
of computer network protection. It presents theoretical frameworks
and the latest research findings in network security technologies,
while analyzing malicious threats which can compromise network
integrity. It discusses the security and optimization of computer
networks for use in a variety of disciplines and fields. Touching
on such matters as mobile and VPN security, IP spoofing and
intrusion detection, this edited collection emboldens the efforts
of researchers, academics and network administrators working in
both the public and private sectors. This edited compilation
includes chapters covering topics such as attacks and
countermeasures, mobile wireless networking, intrusion detection
systems, next-generation firewalls, web security and much more.
Information and communication systems are an essential component of
our society, forcing us to become dependent on these
infrastructures. At the same time, these systems are undergoing a
convergence and interconnection process that has its benefits, but
also raises specific threats to user interests. Citizens and
organizations must feel safe when using cyberspace facilities in
order to benefit from its advantages. This book is
interdisciplinary in the sense that it covers a wide range of
topics like network security threats, attacks, tools and procedures
to mitigate the effects of malware and common network attacks,
network security architecture and deep learning methods of
intrusion detection.
This book offers a comprehensive overview of Software-Defined
Network (SDN) based ad-hoc network technologies and exploits recent
developments in this domain, with a focus on emerging technologies
in SDN based ad-hoc networks. The authors offer practical and
innovative applications in Network Security, Smart Cities,
e-health, and Intelligent Systems. This book also addresses several
key issues in SDN energy-efficient systems, the Internet of Things,
Big Data, Cloud Computing and Virtualization, Machine Learning,
Deep Learning, and Cryptography. The book includes different ad hoc
networks such as MANETs and VANETs, along with a focus on
evaluating and comparing existing SDN-related research on various
parameters. The book provides students, researchers, and practicing
engineers with an expert guide to the fundamental concepts,
challenges, architecture, applications, and state-of-the-art
developments in the field.
This book offers a comprehensive overview of Software-Defined
Network (SDN) based ad-hoc network technologies and exploits recent
developments in this domain, with a focus on emerging technologies
in SDN based ad-hoc networks. The authors offer practical and
innovative applications in Network Security, Smart Cities,
e-health, and Intelligent Systems. This book also addresses several
key issues in SDN energy-efficient systems, the Internet of Things,
Big Data, Cloud Computing and Virtualization, Machine Learning,
Deep Learning, and Cryptography. The book includes different ad hoc
networks such as MANETs and VANETs, along with a focus on
evaluating and comparing existing SDN-related research on various
parameters. The book provides students, researchers, and practicing
engineers with an expert guide to the fundamental concepts,
challenges, architecture, applications, and state-of-the-art
developments in the field.
A smart city utilizes ICT technologies to improve the working
effectiveness, share various data with the citizens, and enhance
political assistance and societal wellbeing. The fundamental needs
of a smart and sustainable city are utilizing smart technology for
enhancing municipal activities, expanding monetary development, and
improving citizens' standards of living. Data-Driven Mathematical
Modeling in Smart Cities discusses new mathematical models in smart
and sustainable cities using big data, visualization tools in
mathematical modeling, machine learning-based mathematical
modeling, and more. It further delves into privacy and ethics in
data analysis. Covering topics such as deep learning,
optimization-based data science, and smart city automation, this
premier reference source is an excellent resource for
mathematicians, statisticians, computer scientists, civil
engineers, government officials, students and educators of higher
education, librarians, researchers, and academicians.
With the field of computational statistics growing rapidly, there
is a need for capturing the advances and assessing their impact.
Advances in simulation and graphical analysis also add to the pace
of the statistical analytics field. Computational statistics play a
key role in financial applications, particularly risk management
and derivative pricing, biological applications including
bioinformatics and computational biology, and computer network
security applications that touch the lives of people. With high
impacting areas such as these, it becomes important to dig deeper
into the subject and explore the key areas and their progress in
the recent past. Methodologies and Applications of Computational
Statistics for Machine Intelligence serves as a guide to the
applications of new advances in computational statistics. This text
holds an accumulation of the thoughts of multiple experts together,
keeping the focus on core computational statistics that apply to
all domains. Covering topics including artificial intelligence,
deep learning, and trend analysis, this book is an ideal resource
for statisticians, computer scientists, mathematicians, lecturers,
tutors, researchers, academic and corporate libraries,
practitioners, professionals, students, and academicians.
Steganography is the art of secret writing. The purpose of
steganography is to hide the presence of a message from the
intruder by using state-of-the-art methods, algorithms,
architectures, models, and methodologies in the domains of cloud,
internet of things (IoT), and the Android platform. Though security
controls in cloud computing, IoT, and Android platforms are not
much different than security controls in an IT environment, they
might still present different types of risks to an organization
than the classic IT solutions. Therefore, a detailed discussion is
needed in case there is a breach in security. It is important to
review the security aspects of cloud, IoT, and Android platforms
related to steganography to determine how this new technology is
being utilized and improved continuously to protect information
digitally. The benefits and challenges, along with the current and
potential developments for the future, are important keystones in
this critical area of security research. Multidisciplinary Approach
to Modern Digital Steganography reviews the security aspects of
cloud, IoT, and Android platforms related to steganography and
addresses emerging security concerns, new algorithms, and case
studies in the field. Furthermore, the book presents a new approach
to secure data storage on cloud infrastructure and IoT along with
including discussions on optimization models and security controls
that could be implemented. Other important topics include data
transmission, deep learning techniques, machine learning, and both
image and text stenography. This book is essential for forensic
engineers, forensic analysts, cybersecurity analysts, cyber
forensic examiners, security engineers, cybersecurity network
analysts, cyber network defense analysts, and digital forensic
examiners along with practitioners, researchers, academicians, and
students interested in the latest techniques and state-of-the-art
methods in digital steganography.
With the field of computational statistics growing rapidly, there
is a need for capturing the advances and assessing their impact.
Advances in simulation and graphical analysis also add to the pace
of the statistical analytics field. Computational statistics play a
key role in financial applications, particularly risk management
and derivative pricing, biological applications including
bioinformatics and computational biology, and computer network
security applications that touch the lives of people. With high
impacting areas such as these, it becomes important to dig deeper
into the subject and explore the key areas and their progress in
the recent past. Methodologies and Applications of Computational
Statistics for Machine Intelligence serves as a guide to the
applications of new advances in computational statistics. This text
holds an accumulation of the thoughts of multiple experts together,
keeping the focus on core computational statistics that apply to
all domains. Covering topics including artificial intelligence,
deep learning, and trend analysis, this book is an ideal resource
for statisticians, computer scientists, mathematicians, lecturers,
tutors, researchers, academic and corporate libraries,
practitioners, professionals, students, and academicians.
Steganography is the art of secret writing. The purpose of
steganography is to hide the presence of a message from the
intruder by using state-of-the-art methods, algorithms,
architectures, models, and methodologies in the domains of cloud,
internet of things (IoT), and the Android platform. Though security
controls in cloud computing, IoT, and Android platforms are not
much different than security controls in an IT environment, they
might still present different types of risks to an organization
than the classic IT solutions. Therefore, a detailed discussion is
needed in case there is a breach in security. It is important to
review the security aspects of cloud, IoT, and Android platforms
related to steganography to determine how this new technology is
being utilized and improved continuously to protect information
digitally. The benefits and challenges, along with the current and
potential developments for the future, are important keystones in
this critical area of security research. Multidisciplinary Approach
to Modern Digital Steganography reviews the security aspects of
cloud, IoT, and Android platforms related to steganography and
addresses emerging security concerns, new algorithms, and case
studies in the field. Furthermore, the book presents a new approach
to secure data storage on cloud infrastructure and IoT along with
including discussions on optimization models and security controls
that could be implemented. Other important topics include data
transmission, deep learning techniques, machine learning, and both
image and text stenography. This book is essential for forensic
engineers, forensic analysts, cybersecurity analysts, cyber
forensic examiners, security engineers, cybersecurity network
analysts, cyber network defense analysts, and digital forensic
examiners along with practitioners, researchers, academicians, and
students interested in the latest techniques and state-of-the-art
methods in digital steganography.
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