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Machine Learning for Cybersecurity Cookbook

You're reading from   Machine Learning for Cybersecurity Cookbook Over 80 recipes on how to implement machine learning algorithms for building security systems using Python

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Product type Paperback
Published in Nov 2019
Publisher Packt
ISBN-13 9781789614671
Length 346 pages
Edition 1st Edition
Languages
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Author (1):
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Emmanuel Tsukerman Emmanuel Tsukerman
Author Profile Icon Emmanuel Tsukerman
Emmanuel Tsukerman
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Toc

Table of Contents (11) Chapters Close

Preface 1. Machine Learning for Cybersecurity 2. Machine Learning-Based Malware Detection FREE CHAPTER 3. Advanced Malware Detection 4. Machine Learning for Social Engineering 5. Penetration Testing Using Machine Learning 6. Automatic Intrusion Detection 7. Securing and Attacking Data with Machine Learning 8. Secure and Private AI 9. Other Books You May Enjoy Appendix

Malware dynamic analysis

Unlike static analysis, dynamic analysis is a malware analysis technique in which the expert executes the sample, and then studies the sample's behavior as it is being run. The main advantage of dynamic analysis over static is that it allows you to bypass obfuscation by simply observing how a sample behaves, rather than trying to decipher the sample's contents and behavior. Since malware is intrinsically unsafe, researchers resort to executing samples in a virtual machine (VM). This is called sandboxing.

Getting ready

One of the most prominent tools for automating the analysis of samples in a VM is Cuckoo Sandbox. The initial installation of Cuckoo Sandbox is straightforward; simply run the...

You have been reading a chapter from
Machine Learning for Cybersecurity Cookbook
Published in: Nov 2019
Publisher: Packt
ISBN-13: 9781789614671
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