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Mastering Machine Learning for Penetration Testing

You're reading from   Mastering Machine Learning for Penetration Testing Develop an extensive skill set to break self-learning systems using Python

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788997409
Length 276 pages
Edition 1st Edition
Languages
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Author (1):
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Chiheb Chebbi Chiheb Chebbi
Author Profile Icon Chiheb Chebbi
Chiheb Chebbi
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Toc

Table of Contents (13) Chapters Close

Preface 1. Introduction to Machine Learning in Pentesting 2. Phishing Domain Detection FREE CHAPTER 3. Malware Detection with API Calls and PE Headers 4. Malware Detection with Deep Learning 5. Botnet Detection with Machine Learning 6. Machine Learning in Anomaly Detection Systems 7. Detecting Advanced Persistent Threats 8. Evading Intrusion Detection Systems 9. Bypassing Machine Learning Malware Detectors 10. Best Practices for Machine Learning and Feature Engineering 11. Assessments 12. Other Books You May Enjoy

Chapter 1 – Introduction to Machine Learning in Pentesting

  1. Although machine learning is an interesting concept, there are limited business applications in which it is useful.

False

  1. Machine learning applications are too complex to run in the cloud.

False

  1. For two runs of k-means clustering, is it expected to get the same clustering
    results?

No

  1. Predictive models having target attributes with discrete values can be termed as:

Classification models

  1. Which of the following techniques perform operations similar to dropouts in a
    neural network?

Bagging

  1. Which architecture of a neural network would be best suited for solving an image recognition problem?

A convolutional neural network

  1. How does deep learning differ from conventional machine learning?

Deep learning algorithms can handle more data and run with less supervision from data scientists.

  1. Which of the following...
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