|Instructor||Gang Wang (firstname.lastname@example.org)|
|Time/Location||Tuesday/Thursday 3:30 PM - 4:45 PM in McBryde Hall 226|
|Office Hour||By appointment. My CRC office is in KnowledgeWorks II, room 2223 (Reachable via CRC shuttle)|
|Text Book||We will focus on reading research papers. There is no required textbook.|
08/15/17: The class is full right now. If you want to join the class, please use the waiting list and attend the first class during week-1.
Machine learning has become a mainstream tool that significantly extends the capabilities of data-driven systems in a variety of areas. This class will focus on understanding the recent inter-play between machine learning and security. 1) machine learning is a useful technique to build new solutions for many security problems. 2) Similarly, attackers may also use machine learning to launch more intelligent attacks. 3) machine learning itself can introduce a whole new class of risks, allowing adversaries to manipulate the machine learning process and the outcome.
This is not a typical machine learning class: we will not focus on developing new theories or methods in machine learning. Instead, we will study the state of the art in applied machine learning in security related topics. We will focus on understanding the best, most creative ways to apply existing machine learning tools and techniques as well as their limitations and potential risks. In this class, we will read a number of technical papers, and work on a research project in teams of 2-3 students. The goal of the project is to extend current machine learning techniques to new problems, with the end goal of producing real and publishable results by the end of the semester. In addition, students are expected to gain experience in two valuable skills: quickly reading technical papers (without sacrificing understanding), and giving good public presentations.
Participation: students are required to attend all lectures, read all required papers and participate in paper discussions both online and in-class.
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/privacy. Each team will give a short talk in the midterm and have 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. Each student will cover 1-2 papers depending on the class size.
|Aug 29, 31||ML for Attack|
|Sep 05, 07||ML for Attack|
|Sep 12, 14||ML for Attack||Proposal due (Tue 11:59pm)|
|Sep 19, 21||ML for Defense|
|Sep 26, 28||Adversarial ML|
|Oct 03, 05||Adversarial ML|
|Oct 10, 12||
|Oct 17, 19||ML for Defense|
|Oct 24, 26||
|Oct 31, Nov 02||ML Usability|
|Nov 07, 09||ML for Defense/Attack|
|Nov 14, 16||Adversarial ML|
|Nov 21, 23||Thanksgiving Holiday|
|Nov 28, 30||Adversarial ML|
|Dec 5, 7||ML for Defense|
|Dec 12, 14||No class, working on your project (Dec 12); Reading Day (Dec 14); Final (1:05-3:05PM, Dec 20, MCB 226)|
|Class attendance and participation||10%|
|Paper discussion online||20%|
|Paper presentation in class||15%|
|Project: midterm presentation||10%|
|Project: final presentation||20%|
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. All fractions will be rounded up.
Please use this discussion section in Canvas to post comments about the papers you read. The discussion will count for 20% of your final score.
Late Policy: All the deadlines are hard deadlines. Any late submissions will be subject to score reduction: if you submit within 3 days (72 hours) after the deadline, your score will be 0.5*(your raw score). If you submit after 3 days, the score will be 0.
Academic Integrity: Virginia Tech Honor Code applies to this course. It describes the expectations for academic integrity in this course. 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. You are encouraged to post your questions to CANVAS, and are also encouraged to answer questions posted by other students. However, you may not give or receive help from others with writing your program code or your answers to any assignment or test. 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 existing work, please make the proper citation. All electronic work submitted for this course is archived and subjected to automatic plagiarism detection and cheating analysis. Whenever in doubt, please seek help from the instructor. I will not hesitate to report any incidents of academic dishonesty to the graduate school or honor system. For more information on the Graduate Honor Code, please refer to the GHS Constitution. More information about the Virginia Tech Honor Code can be found here.
When presenting research papers in the class, you may not use the authors' slides directly. Please make your own slides and add your own thoughts.
Special Accommodations: If you need special accommodations because of a disability, please contact the instructor in the first week of classes.