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Security and Privacy in Digital Economy - First International Conference, SPDE 2020, Quzhou, China, October 30 - November 1, 2020, Proceedings (Paperback, 1st ed. 2020)
Shui Yu, Peter Mueller, Jiangbo Qian
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R3,108
Discovery Miles 31 080
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Ships in 10 - 15 working days
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This book constitutes the refereed proceedings of the First
International Conference on Security and Privacy in Digital
Economy, SPDE 2020, held in Quzhou, China, in October 2020*. The 49
revised full papers and 2 short papers were carefully reviewed and
selected from 132 submissions. The papers are organized in topical
sections: cyberspace security, privacy protection, anomaly and
intrusion detection, trust computation and forensics, attacks and
countermeasures, covert communication, security protocol, anonymous
communication, security and privacy from social science. *The
conference was held virtually due to the COVID-19 pandemic.
This book covers and makes four major contributions: 1) analyzing
and surveying the pros and cons of current approaches for
identifying rumor sources on complex networks; 2) proposing a novel
approach to identify rumor sources in time-varying networks; 3)
developing a fast approach to identify multiple rumor sources; 4)
proposing a community-based method to overcome the scalability
issue in this research area. These contributions enable rumor
source identification to be applied effectively in real-world
networks, and eventually diminish rumor damages, which the authors
rigorously illustrate in this book. In the modern world, the
ubiquity of networks has made us vulnerable to various risks. For
instance, viruses propagate throughout the Internet and infect
millions of computers. Misinformation spreads incredibly fast in
online social networks, such as Facebook and Twitter. Infectious
diseases, such as SARS, H1N1 or Ebola, have spread geographically
and killed hundreds of thousands people. In essence, all of these
situations can be modeled as a rumor spreading through a network,
where the goal is to find the source of the rumor so as to control
and prevent network risks. So far, extensive work has been done to
develop new approaches to effectively identify rumor sources.
However, current approaches still suffer from critical weaknesses.
The most serious one is the complex spatiotemporal diffusion
process of rumors in time-varying networks, which is the bottleneck
of current approaches. The second problem lies in the expensively
computational complexity of identifying multiple rumor sources. The
third important issue is the huge scale of the underlying networks,
which makes it difficult to develop efficient strategies to quickly
and accurately identify rumor sources. These weaknesses prevent
rumor source identification from being applied in a broader range
of real-world applications. This book aims to analyze and address
these issues to make rumor source identification more effective and
applicable in the real world. The authors propose a novel reverse
dissemination strategy to narrow down the scale of suspicious
sources, which dramatically promotes the efficiency of their
method. The authors then develop a Maximum-likelihood estimator,
which can pin point the true source from the suspects with high
accuracy. For the scalability issue in rumor source identification,
the authors explore sensor techniques and develop a community
structure based method. Then the authors take the advantage of the
linear correlation between rumor spreading time and infection
distance, and develop a fast method to locate the rumor diffusion
source. Theoretical analysis proves the efficiency of the proposed
method, and the experiment results verify the significant
advantages of the proposed method in large-scale networks. This
book targets graduate and post-graduate students studying computer
science and networking. Researchers and professionals working in
network security, propagation models and other related topics, will
also be interested in this book.
This book covers three major parts of Big Data: concepts, theories
and applications. Written by world-renowned leaders in Big Data,
this book explores the problems, possible solutions and directions
for Big Data in research and practice. It also focuses on high
level concepts such as definitions of Big Data from different
angles; surveys in research and applications; and existing tools,
mechanisms, and systems in practice. Each chapter is independent
from the other chapters, allowing users to read any chapter
directly. After examining the practical side of Big Data, this book
presents theoretical perspectives. The theoretical research ranges
from Big Data representation, modeling and topology to distribution
and dimension reducing. Chapters also investigate the many
disciplines that involve Big Data, such as statistics, data mining,
machine learning, networking, algorithms, security and differential
geometry. The last section of this book introduces Big Data
applications from different communities, such as business,
engineering and science. Big Data Concepts, Theories and
Applications is designed as a reference for researchers and
advanced level students in computer science, electrical engineering
and mathematics. Practitioners who focus on information systems,
big data, data mining, business analysis and other related fields
will also find this material valuable.
This brief provides readers a complete and self-contained resource
for information about DDoS attacks and how to defend against them.
It presents the latest developments in this increasingly crucial
field along with background context and survey material. The book
also supplies an overview of DDoS attack issues, DDoS attack
detection methods, DDoS attack source traceback, and details on how
hackers organize DDoS attacks. The author concludes with future
directions of the field, including the impact of DDoS attacks on
cloud computing and cloud technology. The concise yet comprehensive
nature of this brief makes it an ideal reference for researchers
and professionals studying DDoS attacks. It is also a useful
resource for graduate students interested in cyberterrorism and
networking.
Networking for Big Data supplies an unprecedented look at
cutting-edge research on the networking and communication aspects
of Big Data. Starting with a comprehensive introduction to Big Data
and its networking issues, it offers deep technical coverage of
both theory and applications. The book is divided into four
sections: introduction to Big Data, networking theory and design
for Big Data, networking security for Big Data, and platforms and
systems for Big Data applications. Focusing on key networking
issues in Big Data, the book explains network design and
implementation for Big Data. It examines how network topology
impacts data collection and explores Big Data storage and resource
management. Addresses the virtual machine placement problem
Describes widespread network and information security technologies
for Big Data Explores network configuration and flow scheduling for
Big Data applications Presents a systematic set of techniques that
optimize throughput and improve bandwidth for efficient Big Data
transfer on the Internet Tackles the trade-off problem between
energy efficiency and service resiliency The book covers
distributed Big Data storage and retrieval as well as security,
trust, and privacy protection for Big Data collection, storage, and
search. It discusses the use of cloud infrastructures and
highlights its benefits to overcome the identified issues and to
provide new approaches for managing huge volumes of heterogeneous
data. The text concludes by proposing an innovative user data
profile-aware policy-based network management framework that can
help you exploit and differentiate user data profiles to achieve
better power efficiency and optimized resource management.
This book covers three major parts of Big Data: concepts, theories
and applications. Written by world-renowned leaders in Big Data,
this book explores the problems, possible solutions and directions
for Big Data in research and practice. It also focuses on high
level concepts such as definitions of Big Data from different
angles; surveys in research and applications; and existing tools,
mechanisms, and systems in practice. Each chapter is independent
from the other chapters, allowing users to read any chapter
directly. After examining the practical side of Big Data, this book
presents theoretical perspectives. The theoretical research ranges
from Big Data representation, modeling and topology to distribution
and dimension reducing. Chapters also investigate the many
disciplines that involve Big Data, such as statistics, data mining,
machine learning, networking, algorithms, security and differential
geometry. The last section of this book introduces Big Data
applications from different communities, such as business,
engineering and science. Big Data Concepts, Theories and
Applications is designed as a reference for researchers and
advanced level students in computer science, electrical engineering
and mathematics. Practitioners who focus on information systems,
big data, data mining, business analysis and other related fields
will also find this material valuable.
This book aims to sort out the clear logic of the development of
machine learning-driven privacy preservation in IoTs, including the
advantages and disadvantages, as well as the future directions in
this under-explored domain. In big data era, an increasingly
massive volume of data is generated and transmitted in Internet of
Things (IoTs), which poses great threats to privacy protection.
Motivated by this, an emerging research topic, machine
learning-driven privacy preservation, is fast booming to address
various and diverse demands of IoTs. However, there is no existing
literature discussion on this topic in a systematically manner. The
issues of existing privacy protection methods (differential
privacy, clustering, anonymity, etc.) for IoTs, such as low data
utility, high communication overload, and unbalanced trade-off, are
identified to the necessity of machine learning-driven privacy
preservation. Besides, the leading and emerging attacks pose
further threats to privacy protection in this scenario. To mitigate
the negative impact, machine learning-driven privacy preservation
methods for IoTs are discussed in detail on both the advantages and
flaws, which is followed by potentially promising research
directions. Readers may trace timely contributions on machine
learning-driven privacy preservation in IoTs. The advances cover
different applications, such as cyber-physical systems, fog
computing, and location-based services. This book will be of
interest to forthcoming scientists, policymakers, researchers, and
postgraduates.
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Tools for Design, Implementation and Verification of Emerging Information Technologies - 17th EAI International Conference, TridentCom 2022, Melbourne, Australia, November 23-25, 2022, Proceedings (1st ed. 2023)
Shui Yu, Bruce Gu, Youyang Qu, Xiaodong Wang
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R1,776
Discovery Miles 17 760
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Ships in 10 - 15 working days
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This book constitutes the refereed post-conference proceedings of
the 17th EAI International Conference on Tools for Design,
Implementation and Verification of Emerging Information
Technologies, TridentCom 2022, which was held in Melbourne,
Australia, in November 23-25,2022. The 11 full papers were selected
from 30 submissions and deal the emerging technologies of big data,
cyber-physical systems and computer communications. The papers are
grouped in thematical sessions on network security; network
communication; network services; mobile and ad hoc networks;
blockchain; machine learning.
An Introduction to the Machine Learning Empowered Intelligent Data
Center Networking Fundamentals of Machine Learning in Data Center
Networks. This book reviews the common learning paradigms that are
widely used in data centernetworks, and offers an introduction to
data collection and data processing in data centers. Additionally,
it proposes a multi-dimensional and multi-perspective solution
quality assessment system called REBEL-3S. The book offers readers
a solid foundation for conducting research in the field of
AI-assisted data center networks. Comprehensive Survey of
AI-assisted Intelligent Data Center Networks. This book
comprehensively investigates the peer-reviewed literature published
in recent years. The wide range of machine learning techniques is
fully reflected to allow fair comparisons. In addition, the book
provides in-depth analysis and enlightening discussions on the
effectiveness of AI in DCNs from various perspectives, covering
flow prediction, flow classification, load balancing, resource
management, energy management, routing optimization, congestion
control, fault management, and network security.Provides a Broad
Overview with Key Insights. This book introduces several novel
intelligent networking concepts pioneered by real-world industries,
such as Knowledge Defined Networks, Self-Driving Networks,
Intent-driven Networks and Intent-based Networks. Moreover, it
shares unique insights into the technological evolution of the
fusion of artificial intelligence and data center networks,
together with selected challenges and future research
opportunities.
This book presents the data privacy protection which has been
extensively applied in our current era of big data. However,
research into big data privacy is still in its infancy. Given the
fact that existing protection methods can result in low data
utility and unbalanced trade-offs, personalized privacy protection
has become a rapidly expanding research topic.In this book, the
authors explore emerging threats and existing privacy protection
methods, and discuss in detail both the advantages and
disadvantages of personalized privacy protection. Traditional
methods, such as differential privacy and cryptography, are
discussed using a comparative and intersectional approach, and are
contrasted with emerging methods like federated learning and
generative adversarial nets. The advances discussed cover various
applications, e.g. cyber-physical systems, social networks, and
location-based services. Given its scope, the book is of interest
to scientists, policy-makers, researchers, and postgraduates alike.
This book presents the data privacy protection which has been
extensively applied in our current era of big data. However,
research into big data privacy is still in its infancy. Given the
fact that existing protection methods can result in low data
utility and unbalanced trade-offs, personalized privacy protection
has become a rapidly expanding research topic.In this book, the
authors explore emerging threats and existing privacy protection
methods, and discuss in detail both the advantages and
disadvantages of personalized privacy protection. Traditional
methods, such as differential privacy and cryptography, are
discussed using a comparative and intersectional approach, and are
contrasted with emerging methods like federated learning and
generative adversarial nets. The advances discussed cover various
applications, e.g. cyber-physical systems, social networks, and
location-based services. Given its scope, the book is of interest
to scientists, policy-makers, researchers, and postgraduates alike.
Networking for Big Data supplies an unprecedented look at
cutting-edge research on the networking and communication aspects
of Big Data. Starting with a comprehensive introduction to Big Data
and its networking issues, it offers deep technical coverage of
both theory and applications. The book is divided into four
sections: introduction to Big Data, networking theory and design
for Big Data, networking security for Big Data, and platforms and
systems for Big Data applications. Focusing on key networking
issues in Big Data, the book explains network design and
implementation for Big Data. It examines how network topology
impacts data collection and explores Big Data storage and resource
management. Addresses the virtual machine placement problem
Describes widespread network and information security technologies
for Big Data Explores network configuration and flow scheduling for
Big Data applications Presents a systematic set of techniques that
optimize throughput and improve bandwidth for efficient Big Data
transfer on the Internet Tackles the trade-off problem between
energy efficiency and service resiliency The book covers
distributed Big Data storage and retrieval as well as security,
trust, and privacy protection for Big Data collection, storage, and
search. It discusses the use of cloud infrastructures and
highlights its benefits to overcome the identified issues and to
provide new approaches for managing huge volumes of heterogeneous
data. The text concludes by proposing an innovative user data
profile-aware policy-based network management framework that can
help you exploit and differentiate user data profiles to achieve
better power efficiency and optimized resource management.
This brief presents emerging and promising communication methods
for network reliability via delay tolerant networks (DTNs).
Different from traditional networks, DTNs possess unique features,
such as long latency and unstable network topology. As a result,
DTNs can be widely applied to critical applications, such as space
communications, disaster rescue, and battlefield communications.
The brief provides a complete investigation of DTNs and their
current applications, from an overview to the latest development in
the area. The core issue of data forward in DTNs is tackled,
including the importance of social characteristics, which is an
essential feature if the mobile devices are used for human
communication. Security and privacy issues in DTNs are discussed,
and future work is also discussed.
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