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Project

This year, students enrolled in this course will be required to finish a course project. The goals of the course project include, but not limited to:

  • to study a new security topic. As security is growing rapidly and new trends are emerging, so it's important for us to understand the new frontiers; and
  • to have an opportunity for you to apply what you have learned in the class about the basic principals of security to new problems.

Project Topics

Although there are many new emerging security directions, but to keep us aligned, we will be concentrating on machine learning security this year. If you really want to choose another topic, you should discuss with us before the project and get our approval.

There are roughly two directions for machine learning security, we'll give you some references as starting points, but you are free to propose your own ideas:

  • the security of machine learning systems: here is a good starting point;
  • to apply machine learning techniques to security problems: your can start with this.

To inspire ideas, you might also look at recent machine learning security publications from top-tier security conferences, as well as other resources below.

  • CCS: ACM Conference on Computer and Communications Security
  • SECURITY: USENIX Security Symposium
  • S & P: IEEE Symposium on Security and Privacy
  • NDSS: The Network and Distributed System Security Symposium

Collaboration Policy

You should work in a group of no more than 3 students. Working individually is allowable but not encouraged, not only for the engineering effort to finish the project is considerable, but also team collaboration is an important skill in security research (and all software development) practice.

Important Dates

There will be project proposal, project report and project presentation. The dates for these items are:

  • Project proposal: April 22
  • Project report: June 11
  • Project presentation: June 18

Project Proposal

The project proposal should be of no more than one page, and should be written using this template. Your project proposal should describe:

  • What is the problem that you will be investigating? Why is it interesting?
  • What reading will you examine to provide context and background?
  • What data will you use to conduct the experiment? If you are collecting new data, how will you do it?
  • What method or algorithm are you proposing?
  • If there are existing implementations, will you use them and how?
  • How do you plan to improve or modify such implementations?
  • You don't have to have an exact answer at this point, but you should have a general sense of how you will approach the problem you are working on.
  • How will you evaluate your results? Qualitatively, what kind of results do you expect (e.g. plots or figures)? Quantitatively, what kind of analysis will you use to evaluate and/or compare your results (e.g. what performance metrics or statistical tests)?

You should submit your proposal before the due date, and you should give an oral presentation of your proposal.

Project Report

Your should finish and submit a write-up as the project report. The report should structured like a paper, using this template. Please make sure to use this template so that the reports are of the uniform style (fonts, color, spacing, etc..). And we'll post all the reports online, so that you can learn from each other's work. If you don't want to your reports posted, please let us know in advance. Your reports should be written in English (preferred) or Chinese.

Your project report should be of 4-8 pages, structured like a research paper. The following is a recommended structure for your report:

  • Title: what the paper is about;
  • Authors: authors and their contact information;
  • Abstract: Briefly describe the background, the problem, approach, and key results. Should be no more than 300 words;
  • Introduction: Describe the problem you are working on, why it's important and challenging, and an overview of your results (and contribution);
  • Related Work: Discuss published work that relates to your project. How is your approach similar or different from others?
  • Methods: Discuss your approach for solving the problems that you set up in the introduction. Why is your approach the right thing to do? Did you consider alternative approaches? You should demonstrate that you have applied ideas and skills built up during the quarter to tackling your problem of choice. It may be helpful to include figures, diagrams, or tables to describe your method or compare it with other methods.
  • Experiments and evaluation: Discuss the experiments that you performed to demonstrate that your approach solves the problem. The exact experiments will vary depending on the project, but you might compare with previously published methods, perform an ablation study to determine the impact of various components of your system, experiment with different architectural choices, use visualization techniques to gain insight into how your model works, discuss common failure modes of your model, etc. You should include graphs, tables, or other figures to illustrate your experimental results.
  • Conclusion: Summarize your key results - what have you learned? Suggest ideas for future extensions or new applications of your ideas.

Project Presentation

There will be a presentation session. Each group is required to finish a 5-minute presentation for their project. Each group member should speak during the presentation.

The following is a suggested structure for the presentation. You don't necessarily have to organize your presentation using these sections in this order, but that would likely be a good starting point for most projects.

  • Problem Statement: Briefly describe the problem your group is tackling. Describe the overall motivation, as well as the input / output of the problem.
  • Technical Challenges: Briefly describe why the problem is technically challenging.
  • Related Works: Briefly in what ways previous works have tackled the technical challenges.
  • Your Approach and Results: Describe your detailed technical approach and innovations. Describe evaluation results (dataset and metric).
  • Broader Impact:How do you expect the impact of your work to be? What can others learn from it or how can they apply it to solve their problems? What are the limitations of your work? What are areas for future improvements?