Many networked computer systems are far too vulnerable to cyber
attacks that can inhibit their functioning, corrupt important data,
or expose private information. Not surprisingly, the field of
cyber-based systems is a fertile ground where many tasks can be
formulated as learning problems and approached in terms of machine
learning algorithms.
This book contains original materials by leading researchers in
the area and covers applications of different machine learning
methods in the reliability, security, performance, and privacy
issues of cyber space. It enables readers to discover what types of
learning methods are at their disposal, summarizing the
state-of-the-practice in this significant area, and giving a
classification of existing work.
Those working in the field of cyber-based systems, including
industrial managers, researchers, engineers, and graduate and
senior undergraduate students will find this an indispensable guide
in creating systems resistant to and tolerant of cyber attacks.
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