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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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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

Questions

  1. Although machine learning is an interesting concept, there are limited business applications in which it is useful. (True | False)
  2. Machine learning applications are too complex to run in the cloud. (True | False)
  3. For two runs of k-means clustering, is it expected to get the same clustering results? (Yes | No)
  4. Predictive models having target attributes with discrete values can be termed as:

(a) Regression models
(b) Classification models

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

(a) Stacking
(b) Bagging
(c) Boosting

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

(a) Convolutional neural network
(b) Recurrent neural network
(c) Multi-Layer Perceptron
(d) Perceptron

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

(a) Deep learning algorithms can handle more data and run with less supervision from data scientists.
(b) Machine learning is simpler, and requires less oversight by data analysts than deep learning does.

(c) There are no real differences between the two; they are the same tool, with different names.

  1. Which of the following is a technique frequently used in machine learning projects?

(a) Classification of data into categories.
(b) Grouping similar objects into clusters.
(c) Identifying relationships between events to predict when one will follow the other.
(d) All of the above.

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