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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

Tackling packed malware

Packing is the compression or encryption of an executable file, distinguished from ordinary compression in that it is typically decompressed during runtime, in memory, as opposed to being decompressed to disk, prior to execution. Packers pose an obfuscation challenge to analysts.

A packer called VMProtect, for example, protects its content from analyst eyes by executing in a virtual environment with a unique architecture, making it a great challenge for anyone to analyze the software.

Amber is a reflective PE packer for bypassing security products and mitigations. It can pack regularly compiled PE files into reflective payloads that can load and execute themselves like a shellcode. It enables stealthy in-memory payload deployment that can be used to bypass anti-virus, firewall, IDS, IPS products, and application whitelisting mitigations. The most commonly...

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