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Published on March 22nd, 2019 📆 | 3091 Views ⚑

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Machine Learning for Malware Detection – 6 – Coding the Classifier – Part 2

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Books I recommend for Penetration Testing and Ethical Hacking:

1. TJ O'Connor - Violent Python: https://amzn.to/31vH2GB
2. Dafydd Stuttard - Web App Hacker' s Handbook (2nd Ed): https://amzn.to/2MRcjk3
3. Peter Yaworski - Web Hacking 101: https://amzn.to/2KTvJSy
4. Jon Erickson - Hacking: The Art of Exploitation (2nd Ed):
https://amzn.to/2WHr3BD
5. Peter Kim - The Hacker Playbook 3 (2018): https://amzn.to/2MQiTXK
6. Peter Yaworski - Real-World Bug Hunting: https://amzn.to/37GB87K

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Short-clips via: https://mixkit.co/
__________

Whatever type of tests you're doing, only perform them in safe and legal environments and with the appropriate permissions.
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In this series you'll learn how to apply machine learning in cybersecurity.

In this video we train a Random Forest classifier on a malware dataset so that it learns to distinguish between legitimate and malware samples. We also look at a confusion matrix for this machine learning algorithm.

References:

Chiheb Chebbi - Mastering Machine Learning for Penetration Testing: https://amzn.to/2EpcBbz

Only perform these types of engagements in safe and legal environments and with the appropriate permissions.

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