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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 FREE CHAPTER 2. Phishing Domain Detection 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

We hope it was easy to go through this chapter. Now, as usual, it is practice time. Your job is to try building your own spam detection system. We will guide you through the questions.

In this chapter's GitHub repository, you will find a dataset collected from research done by Androutsopoulos, J. Koutsias, K.V. Chandrinos, George Paliouras, and C.D. Spyropoulos: An Evaluation of Naive Bayesian Anti-Spam Filtering. Proceedings of the workshop on Machine Learning in the New Information Age, G. Potamias, V. Moustakis and M. van Someren (eds.), 11th European Conference on Machine Learning, Barcelona, Spain, pp. 9-17, 2000.

You can now prepare the data:

  1. The following are some text-cleaning tasks to perform:
    • Clean your texts of stopwords, digits, and punctuation marks.
    • Perform lemmatization.
  2. Create a word dictionary, including their frequencies.
In email texts, you...
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