Presentations

ABSTRACT
Picking an attacker's signals out of billions of log events in near real time from petabyte scale storage is a daunting task, but Microsoft has been using security data science at cloud scale to successfully disrupt attackers. This session will present the latest frameworks, techniques and the machine-learning algorithms that Microsoft uses to protect its infrastructure and customers.

ABSTRACT

Supervised machine learning depends on its "supervision", on the labeled ground truth used to build a machine learning model. We demonstrate that it is possible to dramatically undermine the utility of those models by tampering with the supposedly accurate labels in the training data. That is, if some of the ground "truth" is actually lying, the resulting model may seem, incorrectly, to be uselessly inaccurate. Or, worse, it may seem an accurate model when trained, but be crafted to fail miserably in practice.