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Anomaly detection for monitoring : a statistical approach to time series anomaly detection / Preetam Jinka & Baron Schwartz.

O'Reilly Online Learning: Academic/Public Library Edition Available online

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Format:
Book
Author/Creator:
Jinka, Preetam, author.
Schwartz, Baron, author.
Language:
English
Subjects (All):
Anomaly detection (Computer security).
Computer security.
Computer networks--Security measures.
Computer networks.
Statistics.
Physical Description:
1 online resource (1 volume) : illustrations
Edition:
First edition.
Other Title:
Statistical approach to time series anomaly detection
Place of Publication:
Sebastopol, CA : O'Reilly Media, [2015]
System Details:
text file
Summary:
Monitoring, the practice of observing systems and determining if they're healthy, is hard--and getting harder. In a perfect world, your anomaly detection system would warn you about new behaviors and data patterns in time to fix problems before they happened, and would be completely foolproof, never ringing the alarm bell when it shouldn't. Such a system doesn't exist (yet), but that shouldn't make you lose sight of the fact that better anomaly detection is possible and can provide tremendous operational benefits. This report demystifies the topic and clarifies some of the fundamental choices that you have to make when constructing anomaly detection mechanisms. You'll learn why some approaches to anomaly detection work better than others in certain situations, and why a better solution for some challenges may be within reach after all. Authors Preetam Jinka and Baron Schwartz introduce the various types of monitoring systems, explain the logic behind them, and help you to navigate the labyrinth of current anomaly detection by outlining the tradeoffs associated with different approaches so you can make judgments as you reach each fork in the road.
Notes:
Description based on online resource; title from title page (Safari, viewed July 9, 2018).
Includes bibliographical references.
ISBN:
9781492042341
149204234X
9781491935781
1491935782
OCLC:
1043671371

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