Instructor Gang Wang (gangw@illinois.edu)
Time/Location WF 12:30 PM - 01:45 PM. Zoom information in this Google Doc (need to use your illinois Google App to view)
Office Hour By Appointment

Class Description

In recent years, machine learning has significantly extended the capabilities of data-driven methods to solve new problems in System, Networking, and Security domains. Exciting progress has been made in various machine learning applications ranging from vulnerability discovery and security defense to network protocol design, software testing, and system optimization. In this class, we will examine the most creative and "crazy" ideas of applying machine learning to solve system and security problems. The focus will be on exploring new research directions and understanding the limitations and potential risks of this approach. Students will be expected to read, present, and discuss research papers, and work on an original research project. The goal of the project is to extend machine learning techniques to new problems and produce real and publishable results.

Expected Work

Reading: students will be reading and reviewing all the required papers, and participating in paper discussions during the class.

Participation: students are required to attend all the lectures. Please inform the instructor via email if you cannot make it to the class due to travel or sickness.

Team Project: 2-3 students will form a team to work on a single research project throughout the semester. The project should aim to solve a real problem in the intersection area of machine learning and security/system. Each team will write a project proposal, perform literature surveys, give a short talk in the midterm, and give a final presentation at the end of the semester. Each team is also expected to write up a final project report.

Paper Presentation: students will present papers during the class to lead the discussion.

All deadlines are 11:59 PM (CT) of the specific date.

Class Schedule

Week / Date Papers Deadline
Week 1:
Aug 26
Class overview, background introduction (Gang): Slides
Week 1:
Aug 28
ML for defense (spam, phishing): Slides Claim paper slot
Week 2:
Sep 2
ML for attack (password):
Week 2:
Sep 4
ML for attack (voice, image)
Week 3:
Sep 9
ML for attack (hidden voice)
Week 3:
Sep 11
ML for security (e-crime)
Week 4:
Sep 16
ML for attack (deepfake) Project proposal
Week 4:
Sep 18
ML for defense (deep fake)
Week 5:
Sep 23
ML for defense (intrusion, malware)
Week 5:
Sep 25
ML for defense (malware analysis methodology, biases)
Week 6:
Sep 30
ML for defense (malware/code analysis)
Week 6:
Oct 2
ML for defense (malware authorship attribution)
Week 7:
Oct 7
Midterm project presentation
Week 7:
Oct 9
NLP and security (privacy policies) Midterm report due
Week 8:
Oct 14
ML for attack (TOR)
Week 8:
Oct 16
Attacking ML (trojaning)
Week 9:
Oct 21
Attacking ML (evasion and poisoning)
Week 9:
Oct 23
Securing ML (defense)
Week 10:
Oct 28
ML explanations
Week 10:
Oct 30
ML explanations (cont.)
Week 11:
Nov 4
ML explanations (problems, limitations)
Week 11:
Nov 6
Attacking ML (application 1) Progress update slides
Week 12:
Nov 11
Attacking ML (application 2)
Week 12:
Nov 13
ML debugging (software engineering perspectives)
Week 13:
Nov 18
NL for networking (protocol design, policies)
Week 13:
Nov 20
ML for networking (routing, scheduling)
Week 14:
Nov 25
Fall Break
Week 14:
Nov 27
Fall Break
Week 15:
Dec 2
ML vs. autonomous systems
Week 15:
Dec 4
Blackbox attacks
Week-16:
Dec 9
Final presentation
Week 16:
Dec 11
Exam week, no class meeting, submit final report Final report

Grading

Class attendance and participation5%
Paper reviews 25%
Paper presentation in class10%
Project: proposal 10%
Project: midterm presentation 10%
Project: final presentation 15%
Project: midterm report + progress update slides 10%
Project: final report 15%

To calculate final grades, I simply sum up the points obtained by each student (the points will sum up to some number x out of 100) and then use the following scale to determine the letter grade: [0-60] F, [60-62] D-, [63-66] D, [67-69] D+, [70-72] C-, [73-76] C, [77-79] C+, [80-82] B-, [83-86] B, [87-89] B+, [90-92] A-, [93-100] A. I do not curve the grades in any way.

Paper Review

We read two papers before each class meeting. Before each class, students are expected to read both papers and submit a short review via Piazza. The deadline for the review two papers is 11:50 AM (CT) on the day of class.

  • The review should contain sufficient content (about 200-500 words; it can be longer if needed). The review can focus on the key contributions of the paper, the strengths and weaknesses, or potential issues with the experiment methodologies and results. You can also discuss the practical implications of the paper and suggest new ideas. The review should reflect your own thoughts. All the students will post the reviews under the given paper's Piazza thread. If you are the first to review the paper, you get to summarize the paper and comment on the key contributions. Other students who come later should avoid repeating the same arguments/comments that the previous reviews have already covered. Each review needs to have some original comments that are different from others.

Policies

Late Policy: All the deadlines are hard deadlines. Any late submissions will be subject to point reduction. For paper reviews, and project-related assignments: submitting within 3 days (72 hours) after the deadline = 50% of the points. This policy does not apply to the final project report, for which a late submission is not allowed.

Academic Integrity:

Students must follow the university's guidelines on academic conduct (quick link). This course will have a zero-tolerance policy regarding plagiarism. You (or your team) should complete all the assignments and project tasks on your own. When you use the code or tools developed by other people, please acknowledge the source. If an idea or a concept used in your project has been proposed by others, please make the proper citation. All electronic work submitted for this course will be archived and subjected to automatic plagiarism detection. Whenever in doubt, please seek clarifications from the instructor. Students who violate Academic Integrity policies will be immediately reported to the department and the college.

When presenting research papers in the class, you may NOT use the authors' slides directly. Please make your own slides.

Special Accommodations: If you need special accommodations because of a disability, please contact the instructor in the first week of classes.

Diminished mental health, including significant stress, mood changes, excessive worry, substance/alcohol abuse, or problems with eating and/or sleeping can interfere with optimal academic performance, social development, and emotional wellbeing. The University of Illinois offers a variety of confidential services including individual and group counseling, crisis intervention, psychiatric services, and specialized screenings at no additional cost. If you or someone you know experiences any of the above mental health concerns, it is strongly encouraged to contact or visit any of the University’s resources provided below. Getting help is a smart and courageous thing to do -- for yourself and for those who care about you.
Counseling Center: 217-333-3704, 610 East John Street Champaign, IL 61820
McKinley Health Center:217-333-2700, 1109 South Lincoln Avenue, Urbana, Illinois 61801