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Applying AI to User Behavior Security Analytics & Threat Intelligence at #HITB2018AMS

posted onMarch 20, 2018
by l33tdawg

Amsterdam – 19 March 2018: User behavior analytics (UBA) solutions typically applies machine learning algorithms to detect abnormal user activities and the market is continuing to expand rapidly with vendor and open-source UBA tech to help organisations identify ‘unknown unknowns’ for further investigation. A key to successfully implementing these solutions requires advanced understanding of the underlying technology, concepts and risks involved.

Eugene Neyolov, Head of R&D at ERPScan has been building a unique machine learning platform for vulnerability management and security monitoring and will be sharing his experience and research at the 9th annual Hack in the Box Conference in Amsterdam next month. His presentation, titled, “The Odd One: Applying Machine Learning to User Behavior Anomaly Analysis” will cover various methods and ideas for anomaly detection solutions using state-of-the-art neural networks and machine learning algorithms to understand user behavior trends and find abnormal activity.

Full details on his presentation can be found here:

Abnormal user behavior which turns out to be malicious or criminal has a significant impact on public or shared computers including hosting providers. Sarah Brown, an independent researcher based in The Hague and Dr. Dhia Mahjoub, Head of Security Research at Cisco Umbrella (OpenDNS) will discuss how Dutch hosting providers have been abused by criminals in their paper “Privacy and Protection for Criminals: Behaviors and Patterns of Rogue Hosting Providers”. Sarah’s and Dr. Dhia’’s presentation summary can be found here:



HITB hitb2018ams HITBSecConf Security

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