(Ebook) Practical machine learning a new look at anomaly detection 1st Edition by Ted Dunning, Ellen Friedman ISBN 9781491911600 1491911603

(Ebook) Practical machine learning a new look at anomaly detection 1st Edition by Ted Dunning, Ellen Friedman ISBN 9781491911600 1491911603

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ISBN 10: 1491911603
ISBN 13: 9781491911600
Author: Ted Dunning, Ellen Friedman

(Ebook) Practical machine learning a new look at anomaly detection 1st Edition Table of contents:

1. Looking Toward the Future

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

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

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

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

6. No Phishing Allowed!

The Phishing Attack

The No-Phishing-Allowed Anomaly Detector

How the Model Works

Putting It All Together

7. Anomaly Detection for the Future

A. Additional Resources

GitHub

Apache Mahout Open Source Project

Additional Publications

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Tags: Ted Dunning, Ellen Friedman, Practical machine learning, anomaly detection