Information Security (Undergraduate)
Security Analytics (Graduate) [Sample Syllabus] [Fall 22] [Fall 21] [Fall 20] [Fall 19]

This graduate-level course will provide students with materials to discuss the intersection of two ubiquitous concepts: Security and Machine Learning. The course is structured in two parts: (1) Machine Learning for Security and (2) Security of Machine Learning Systems. The focus of the first part will be on building a principled understanding of key learning algorithms and techniques, and their applications within the security domain, as well as general questions related to analyzing and handling datasets. The first part will provide students with the necessary background to understand the second half of the course. The second part covers recently discovered security implications of deploying machine learning algorithms in the physical realm. Students will learn about attacks against computer systems leveraging machine learning algorithms, as well as defense techniques to mitigate such attacks during learning and inference.

CS 590: IoT/CPS Security (Graduate) [Sample Syllabus] [Spring 22] [Spring 20]

In this course, we will study the latest research in the design of Internet of Things (IoT) and Cyber-Physical Systems (CPS) and methods for securing them. The course will provide foundations of safety and security of IoT/CPS and covers the topics of policy verification, approaches for designing safe and secure systems, techniques for detecting problems in conventional IoT/CPS design and repairing such problems. Example topics include the security of voice-controlled devices, IoT applications, edge computing, industrial control systems, and autonomous vehicles.