|Instructor||Gang Wang (email@example.com)|
|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|
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.
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 (not including paper reviews).
|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)
|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||
|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||
|Week-16: Dec 9||Final presentation|
|Week 16: Dec 11||Exam week, no class meeting, submit final report||Final report|
|Class attendance and participation||5%|
|Paper presentation in class||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.
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.
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.
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