Network anomaly detection
a machine learning perspective
Our rough guess is there are 85,000 words in this book.
At a pace averaging 250 words per minute, this book will take 5 hours and 40 minutes to read. With a half hour per day, this will take 12 days to read.
How long will it take you?
This book will take an estimated to read at a reading speed averaging words per minute. With 30 minutes per day, this will take to read.
Enter your reading speedYou can take one of our WPM reading speed tests to find your reading speed.
Create a free account to track your reading progress, build your reading list, and set reading goals.
We earn a commission on purchases
Author
Publication
2014 - CRC Press, Taylor & Francis Group, Boca Raton, Florida
Language
English
Word Count
85,000 words, Guess
Page Count
340 pages
Identifiers
- Open LibraryOL31139696M
- ISBN-139781466582088
- OCLC Control Number856570433
- OCLC Control Number860864632
- Library of Congress Control Number2013014913
Classifications
- DDC005.8
- LCCTK5105.59 .B474 2014
Description
"This book discusses detection of anomalies in computer networks from a machine learning perspective. It introduces readers to how computer networks work and how they can be attacked by intruders in search of fame, fortune, or challenge. The reader will learn how one can look for patterns in captured network traffic data to look for anomalous patterns that may correspond to attempts at unauthorized intrusion. The reader will be given a technical and sophisticated description of such algorithms and their applications in the context of intrusion detection in networks"--
Subjects
Topics
Similar Books
Android malware and analysis
Ken Dunham, Shane Hartman, Jose Andre Morales, Manu Quintans, Tim Strazzere
The Alignment Problem: Machine Learning and Human Values
Brian Christian
Introduction to Modern Cryptography
Yehuda Lindell, Jonathan Katz, Jonathan Katz
Securing Social Networks in Cyberspace
Al-Sakib Khan Pathan
Reader Reviews
No reviews yet for this book.
Be the first to share your thoughts!