Software Engineering Institute

This webcast illustrates where machine learning applications can be attacked, the means for carrying out the attack and some mitigations that can be employed. The elements in building and deploying a machine learning application are reviewed, considering both data and processes. The impact of attacks on each element is considered in turn. Special attention is given to transfer learning, a popular way to construct quickly a machine learning application. Mitigations to these attacks are discussed with the engineering tradeoffs between security and accuracy. Finally, the methods by which an attacker could get access to the machine learning system are reviewed.

What attendees will learn:

• What are the new attack surfaces exposed by machine learning application
• What is the tradeoff between security and accuracy in a machine learning application
• How might machine learning applications be attacked