Big data is presenting challenges to cybersecurity. For an example,
the Internet of Things (IoT) will reportedly soon generate a
staggering 400 zettabytes (ZB) of data a year. Self-driving cars
are predicted to churn out 4000 GB of data per hour of driving. Big
data analytics, as an emerging analytical technology, offers the
capability to collect, store, process, and visualize these vast
amounts of data. Big Data Analytics in Cybersecurity examines
security challenges surrounding big data and provides actionable
insights that can be used to improve the current practices of
network operators and administrators. Applying big data analytics
in cybersecurity is critical. By exploiting data from the networks
and computers, analysts can discover useful network information
from data. Decision makers can make more informative decisions by
using this analysis, including what actions need to be performed,
and improvement recommendations to policies, guidelines,
procedures, tools, and other aspects of the network processes.
Bringing together experts from academia, government laboratories,
and industry, the book provides insight to both new and more
experienced security professionals, as well as data analytics
professionals who have varying levels of cybersecurity expertise.
It covers a wide range of topics in cybersecurity, which include:
Network forensics Threat analysis Vulnerability assessment
Visualization Cyber training. In addition, emerging security
domains such as the IoT, cloud computing, fog computing, mobile
computing, and cyber-social networks are examined. The book first
focuses on how big data analytics can be used in different aspects
of cybersecurity including network forensics, root-cause analysis,
and security training. Next it discusses big data challenges and
solutions in such emerging cybersecurity domains as fog computing,
IoT, and mobile app security. The book concludes by presenting the
tools and datasets for future cybersecurity research.
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