The Security of Machine Learning

There is plenty of discussion about how machine learning will be applied to cybersecurity initiatives, but there is precious little conversation about the actual vulnerabilities of these systems themselves. Fortunately, there are a handful of research groups doing the work to assess the threats we face in systematizing data-driven systems. In this webinar, I will introduce you to the main concerns and how to startt thinking about protecting against them.

We will mostly focus on the research findings of the Berryville Institute of Machine Learning. They have conducted a survey of the literature and have identified a taxonomy of the most common kinds of attacks including:

1). Adversarial examples
2). Data poisoning
3). Manipulation of online systems
4). Transfer learning attacks
5). Breaching data confidentiality
6). Undermining data trust