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Books > Computing & IT > Computer communications & networking > Network security
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Malicious Attack Propagation and Source Identification (Hardcover, 1st ed. 2019)
Loot Price: R3,513
Discovery Miles 35 130
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Malicious Attack Propagation and Source Identification (Hardcover, 1st ed. 2019)
Series: Advances in Information Security, 73
Expected to ship within 10 - 15 working days
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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.
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