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When a Pentester meets Machine Learning

José Selvi
In this talk we will see how a penetration tester (#pentester) began using Machine Learning techniques to solve certain problems he faced. We will cover two examples of defensive situation such as false positive reduction in Intrusion Detection Systems by using a One-Class classifier, and a random forest approach to detect hostnames used my malware, in particular, generated by a DGA. For each of these examples, we will cover the path from the first approach we took to the final solution, describing all the mistakes and lessons learned.