Emmanuel Tsukerman

Founder | ML4CS

Dr. Tsukerman graduated from Stanford University and UC Berkeley. In 2017, his machine-learning-based anti-ransomware product won Top 10 Ransomware Products by PC Magazine. In 2018, he designed a machine-learning-based malware detection system for Palo Alto Network's WildFire service (over 30,000 customers).

In 2019, Dr. Tsukerman authored the Machine Learning for Cybersecurity Cookbook and launched the Infosec Skills Cybersecurity Data Science Learning Path.

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Content from Emmanuel Tsukerman

Machine Learning for Red Team Hackers

Learn the various techniques used in hacking machine learning.

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Cybersecurity Data Science

Learn everything you need to employ the latest tools and understand the essentials of cybersecurity data science. Develop skills in malware detection, deep learning technology, intrusion detection, penetration testing and more.

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Evading Machine Learning Malware Classifiers

In this course, you'll learn how to modify malware so it is able to get past neural network and gradient boosting machine learning malware classifiers.

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Introduction to Machine Learning for Red Team Hackers

This course provides a brief overview of the learning path structure, as well as introduction to the code base you'll use.

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Articles from Emmanuel Tsukerman

How artificial intelligence is changing social engineering

June 2, 2020

Social engineers are using artificial intelligence (AI) to further exploit human psychology and gain access to systems and data.

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AI for intrusion detection: Conquering the unknown unknowns

April 3, 2020

An anomaly detection component, which allows detection of previously unknown behavior, is an essential and forward-looking solution to intrusion detection.

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How to catch malware with artificial intelligence

February 13, 2020

There are many variations on the machine learning and data science techniques being utilized, and these account for much of the AV’s effectiveness in dealing with new samples.

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How deep learning is changing cybersecurity

December 17, 2019

The lesson here is clear. The use of machine learning for cybercrime is no longer a future threat. It is present threat, and an important part of cybersecurity.

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