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Machine Learning for Cybersecurity Cookbook

You're reading from  Machine Learning for Cybersecurity Cookbook

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

Table of Contents (11) Chapters close

Preface 1. Machine Learning for Cybersecurity 2. Machine Learning-Based Malware Detection 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 static analysis

In static analysis, we examine a sample without executing it. The amount of information that can be obtained this way is large, ranging from something as simple as the name of the file to the more complex, such as specialized YARA signatures. We will be covering a selection of the large variety of features you could obtain by statically analyzing a sample. Despite its power and convenience, static analysis is no silver bullet, mainly because software can be obfuscated. For this reason, we will be employing dynamic analysis and other techniques in later chapters.

Computing the hash of a sample

Without delving into the intricacies of hashing, a hash is essentially a short and unique string signature....

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