Overview
📢 Page is still under construction; details are tentative.
Course Description
This course focuses on the intersection of two trends:- Computing technology is ubiquitous. Banks, transportation networks, power grids, social media, and even the internet itself all rely on complex computer algorithms, systems, and infrastructure. As the world continues to become more and more interconnected, it becomes increasingly important to ensure that these systems operate correctly, even in the presence of malicious actors.
- Since computing technology is used and deployed across many contexts, the data collected and generated by such systems has also become ubiquitous. AI, which has become increasingly prevalent and powerful, brings the potential to leverage such data for automated insights to help improve cybersecurity. In particular, such data can be used to help defend and secure computing technology from adversaries (and often, such data can help adversaries too).
Learning Objectives
After completing this course, students should be able to:- Describe the various ways in which artificial intelligence (AI) impacts our existing computing technology systems, across a variety of domains and contexts
- Apply concepts from AI and cybersecurity to help build, deploy, and maintain computing systems that operate securely in adversarial environments
- Critically analyze the results, contributions, and implications of newly published research papers in the areas of AI and cybersecurity
- Contribute to projects that develop novel research contributions in AI and cybersecurity
Logistics
The class will meet Tuesdays and Thursdays from 9:30am-10:45am in Engineering 231.Classes primarily consist of two types: "Lectures", which will be led by the instructor and focus on covering background topics, and "Discussions", which will be led by students and consist of discussions of recent research papers in AI and cybersecurity. Before each "Discussion" session, students are expected to read the assigned research papers, write and submit reviews for these papers, and lead or participate in in-class discussions about these research papers.
The course will include two homework assignments. These homework assignments will consist of lightweight coding tasks and will focus on background topics covered in lectures during the first half of the course. The goal is to help students familiarize themselves with code implementations for AI and cybersecurity before they start working on their course projects.
This course also contains a project component. Students will be expected to form small teams (requirements on team sizes TBD) and conduct a small research project in the areas of AI and cybersecurity. When reasonable, students are encouraged to build on the contributions of research papers covered in class for their projects.
Grading
Tentatively, final grades will be weighted by the following components:- Participation in Class Discussions: 25%
- Paper Reviews: 20%
- Homeworks: 10%
- Course Project: 45%
- Project Proposal: 5%
- Midpoint Presentation: 5%
- Final Presentation: 15%
- Final Report: 20%
Course Schedule
Course schedule is tentative. If there are any topics that seem of particular interest, I will try to incorporate them into the schedule.The first half of the class will roughly focus on the fundamentals and applications. Classes will cover the basics of AI, cybersecurity, and how AI is used across various applications. The second half of the class will focus on implications of AI: topics such as usability, secure AI, and the rise of LLMs will be covered.
| Date | Topic | Reading | Notes |
|---|---|---|---|
| Aug 25 | Course Introduction | — | |
| Aug 27 | Lecture: Background on Cybersecurity | — | |
| Sept 1 | Lecture: Background on AI/ML | — | |
| Sept 3 | Lecture: Research Papers and Reviews | — | |
| Sept 8 | Discussion: Best Practices for ML + Cybersecurity | — | |
| Sept 10 | Lecture: AI for Network Security | — | |
| Sept 15 | Discussion: AI for Network Security | — | |
| Sept 17 | Lecture: AI for Software Security | — | |
| Sept 22 | Discussion: AI for Software Security | — | |
| Sept 24 | Lecture: AI for Cyber-Physical Systems (CPS) Security | — | |
| Sept 29 | Discussion: AI for CPS Security | — | |
| Oct 1 | Discussion: Usable Security | — | |
| Oct 6 | Discussion: Malware Detection | — | |
| Oct 8 | Discussion: Web and Application Security | — | |
| Oct 13 | No Class - Fall Recess | — | |
| Oct 15 | Activity: Project Brainstorming and Team Matching | — | |
| Oct 20 | Activity: Project Meetings | — | |
| Oct 22 | Discussion: LLMs and Agents | — | |
| Oct 27 | Discussion: LLMs and Agents II | — | |
| Oct 29 | Lecture: Explainable AI (XAI) | — | |
| Nov 3 | Discussion: XAI for Security | — | |
| Nov 5 | Lecture: Attacking ML | — | |
| Nov 10 | Activity: Midpoint Presentations | — | |
| Nov 12 | Discussion: Attacking ML (Inference) | — | |
| Nov 17 | Discussion: Attacking ML (Privacy) | — | |
| Nov 19 | Discussion: Attacking ML (Supply Chain) | — | |
| Nov 24 | No Class - Class Recess | — | |
| Nov 26 | No Class - Thanksgiving | — | |
| Dec 1 | Discussion: Human Factors | — | |
| Dec 3 | Discussion: Organizational Factors | — | |
| Dec 8 | Final Project Presentations | — |
Helpful links
- 01 Course readings & slides (posted on Blackboard after each lecture)