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Practical Machine Learning A New Look at Anomaly Detection 1st Edition by Ted Dunning, Ellen Friedman ISBN 1491914181 9781491914182

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Author(s): Dunning, Ted;Friedman, B. Ellen
ISBN(s): 9781491914182, 1491914181
Edition: First edition
File Details: PDF, 10.40 MB
Year: 2014
Language: english
SKU: EB-11383618 Category: Tags: , , ,

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ISBN 10: 1491914181 
ISBN 13: 9781491914182
Author: Ted Dunning, Ellen Friedman

Finding Data Anomalies You Didn’t Know to Look For Anomaly detection is the detective work of machine learning: finding the unusual, catching the fraud, discovering strange activity in large and complex datasets. But, unlike Sherlock Holmes, you may not know what the puzzle is, much less what “suspects” you’re looking for. This O’Reilly report uses practical examples to explain how the underlying concepts of anomaly detection work. From banking security to natural sciences, medicine, and marketing, anomaly detection has many useful applications in this age of big data. And the search for anomalies will intensify once the Internet of Things spawns even more new types of data. The concepts described in this report will help you tackle anomaly detection in your own project. Use probabilistic models to predict what’s normal and contrast that to what you observe Set an adaptive threshold to determine which data falls outside of the normal range, using the t-digest algorithm Establish normal fluctuations in complex systems and signals (such as an EKG) with a more adaptive probablistic model Use historical data to discover anomalies in sporadic event streams, such as web traffic Learn how to use deviations in expected behavior to trigger fraud alerts

Practical Machine Learning A New Look at Anomaly Detection 1st Table of contents:

Chapter 1. Looking Toward the Future
Chapter 2. The Shape of Anomaly Detection
Finding “Normal”
If you enjoy math, read this description of a probabilistic model of “normal”…
Human Insight Helps
Finding Anomalies
Once again, if you like math, this description of anomalies is for you…
Take-Home Lesson: Key Steps in Anomaly Detection
A Simple Approach: Threshold Models

Chapter 3. Using t-Digest for Threshold Automation
The Philosophy Behind Setting the Threshold
Using t-Digest for Accurate Calculation of Extreme Quantiles
Issues with Simple Thresholds

Chapter 4. More Complex, Adaptive Models
Windows and Clusters
Matches with the Windowed Reconstruction: Normal Function
Mismatches with the Windowed Reconstruction: Anomalous Function
A Powerful But Simple Technique
Looking Toward Modeling More Problematic Inputs

Chapter 5. Anomalies in Sporadic Events
Counts Don’t Work Well
Arrival Times Are the Key
And Now with the Math…
Event Rate in a Worked Example: Website Traffic Prediction
Extreme Seasonality Effects

Chapter 6. No Phishing Allowed!
The Phishing Attack
The No-Phishing-Allowed Anomaly Detector
How the Model Works
Putting It All Together

Chapter 7. Anomaly Detection for the Future

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Tags: Ted Dunning, Ellen Friedman, Machine Learning, Anomaly Detection