Overview
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
- Instructors:
- Prof. Clement Fung (Lead Instructor)
Contact: clementfung [at] umbc [dot] edu
Office Hours: Thursdays 1-2pm ITE 333 - Sasha Mikhailova (Graduate Teaching Assistant)
Contact: amikhai1 [at] umbc [dot] edu
Office Hours: Wednesdays 1-2pm ITE 334
- Prof. Clement Fung (Lead Instructor)
- Course Page:Blackboard Page
- Paper Reviews: HotCRP
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 of 2-4 members 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: 10%
- Final Report: 25%
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 | 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 | Discussion: Network Security | HW1 out (Sept 11) |
| Sept 15 | Discussion: Software Security | — |
| Sept 17 | Discussion: Web and Application Security | — |
| Sept 22 | Discussion: Smart Home Security | — |
| Sept 24 | Discussion: Cyber-Physical Systems (CPS) Security | HW1 due (Sept 25) HW2 out (Sept 25) |
| Sept 29 | Discussion: Scams and Phishing | — |
| Oct 1 | Discussion: Authentication | — |
| Oct 6 | Discussion: LLMs and Agents | — |
| Oct 8 | Activity: Project Brainstorming and Team Matching | HW2 due (Oct 9) |
| Oct 13 | No Class - Fall Recess | — |
| Oct 15 | Discussion: Humans vs AI | — |
| Oct 20 | Activity: Meet with Project Teams | Project Proposals due (Oct 21) |
| Oct 22 | Lecture: Explainable AI (XAI) for Security | — |
| Oct 27 | Discussion: XAI for Security | — |
| Oct 29 | Lecture: Attacking ML and AI | — |
| Nov 3 | Discussion: Attacking ML (Inference) | — |
| Nov 5 | Discussion: Attacking ML (Supply Chain) | — |
| Nov 10 | Activity: Midpoint Presentations | — |
| Nov 12 | Discussion: Attacking ML (Privacy) | — |
| Nov 17 | Discussion: Human Factors Challenges | — |
| Nov 19 | Discussion: Organizational Challenges
|
— |
| Nov 24 | No Class - Class Recess | — |
| Nov 26 | No Class - Thanksgiving | — |
| Dec 1 | Final Project Presentations | — |
| Dec 3 | Final Project Presentations | — |
| Dec 8 | Course Closing | Project Reports due (TBA; during exams period) |
Policies
Attendance and Remote Participation
In-class attendance is strongly encouraged. For students that require special accommodations, we will accommodate remote participation for lectures, discussions, and activities. (Details TBD)Late Submissions
Due dates and times for homework assignments and paper reviews are firm. If you anticipate any issues, please discuss with me sufficiently advance. Otherwise, late submissions will be penalized by 25% for each day they are late.Accommodations for Students
If you have a disability and have an accommodations letter from the Office of Disability Access and Resources (DAR), I encourage you to discuss your accommodations and needs with me as early in the semester as possible. I will work with you to ensure that accommodations are provided as appropriate. If you suspect that you may have a disability and would benefit from accommodations but are not yet registered with DAR, I encourage you to contact them. More information can be found here: https://sds.umbc.edu/Academic Integrity
This course has a zero tolerance policy on plagiarism and cheating. UMBC's policy on academic integrity will be strictly enforced: https://academicconduct.umbc.edu/The use of generative AI tools for completing homework assignments and paper reviews is strongly discouraged. When appropriate, the use of generative AI tools for projects may be permitted; please contact the instructor to discuss.