Machine Learning is the hottest buzzword. Everybody loves it, everybody sells it. But why is it that while fields such as Computer Vision or Natural Language Processing have stellar achievements, with new record-breaking models published every other week, the Cybersecurity industry staggers behind?

Are Anomaly Detection algorithms – so well beloved for the prevention of attacks and of fraud - really suitable for those intended purposes? What price do we pay for keeping things hushed? Where do our huge datasets fail us? And how, once we spot these issues, might we try and solve them?

In this talk I will go over several points that hold us back, among them: our rapidly changing input data, and who’s to blame for it; the known issues of imbalanced & untagged datasets, and why our solutions for them are insufficient; and, finally, the biggest culprit: the confidential nature of our field, and how it keeps us from being great.

The unspoken problems with Machine Learning in security: let’s talk about them.