Information security attacks can generate vast amounts of data in the form of files, logs, network packets, and more. In this course students will learn how AI systems leverage data to detect and attribute threats such as spam, malware, botnets and network intrusion. The course will examine each of the following stages in an AI workflow in the context of information security applications: data preparation and visualization; feature extraction and selection; model selection, training, tuning and evaluation. The course will also discuss issues of AI explainability and adversarial attacks against AI. Grading will be based on Python programming assignments and an implementation/analysis project.

Faculty and instructors who have taught this course in the past:
David Varodayan