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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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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

Insider threat detection

Insider threat is a complex and growing challenge for employers. It is generally defined as any actions taken by an employee that are potentially harmful to the organization. These can include actions such as unsanctioned data transfer or the sabotaging of resources. Insider threats may manifest in various and novel forms motivated by differing goals, ranging from a disgruntled employee subverting the prestige of an employer, to advanced persistent threats (APT).

The insider risk database of the CERT Program of the Carnegie Mellon University Software Engineering Institute contains the largest public archive of red team scenarios. The simulation is built by combining real-world insider risk case studies with actual neutral clients secretly obtained from a defense corporation. The dataset represents months of traffic in a single engineering company from...

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