This course is about the role of data and data analytics in security and privacy. It covers machine learning and big data analytics applications for various security and privacy problems. Also, it discusses the security and privacy problems associated with machine learning and big data. The topics include using machine learning for making decisions related to security and privacy; misuse detection on social media; tracking technologies; data (de-) anonymization; anomaly detection; privacy-preserving machine learning algorithms, and adversarial machine learning.
Bachelor degree in Computer Science or equivalent. This course requires no prior experience in security and privacy, but assumes the willingness to seek out and read background material as needed. Students are expected to have a basic understanding of deep learning and machine learning ingeneral. Students who enroll for the course are expected to be highly motivated to learn and work hard and be ready to make up for any prerequisite deficiencies they may have.
No required textbook. All course readings will be scientific papers. Each week the instructor lists these papers for students to read before the class.
This is a discussion-based graduate seminar course. You are expected to attend all class meetings and participate actively in the discussion. Students must inform the instructor if they cannot attend a class due to a conference or other major personal or professional obligation.
We will read and discuss 2 papers every week. Every student must complete the assigned reading before class to participate fully in class discussions. To facilitate productive class discussions, students must submit a review of each assigned paper to Canvas on Fridays by 11:59 am. Reviews should consist of three brief paragraphs in students’own words with the following structure:
Paragraph 1:
Explain the problem and motivation.
Explain the main ideas and technical contributions of the work.
Compare this work with the main prior work.
Explain the methodologies used for evaluation.
List three key strengths and three key weaknesses of this paper. Focus should be more on approach and evaluation.
Paragraph 3:List any future work that you might consider in this line of research.
Students will be the discussion leader for two papers' class discussion. As the discussion leader, the student must prepare a 30-minute presentation covering the paper's main technical contributions, plus at least ten discussion questions. Before the beginning of class, the student should upload their slides as a PDF to the Canvas folder for that reading assignment.
Students will work on a major project in groups of sizes 2--3. Students should propose their projects. However, if needed, the instructor will suggest some projects. Here is the timeline for the projects:
Thursday, February 8th (11:59 pm): Every student submits a project idea for their project. Explain your idea in one or two paragraphs: state your research questions, motivation, and general approach.
Friday, February 9th : Students have 5 minutes to present their ideas in class. At the end of this class, students will decide about their projects and their groups.
From Monday, February 12th, to Wednesday, February. 14th: The Instructor and the TA schedule meetings with each group to discuss and refine their idea.
Thursday, February 16th (11:59 pm): Each group submits a finalized version of their project proposal in 2-3 pages. The proposal should state their research questions; hypotheses (if any); general approach; and evaluation metrics. It should also include a timeline with checkpoints and deliverables at those checkpoints. Describe what you hope to accomplish by the end of the semester.
Every week in class: Each group gives an oral progress report (~5-10 minutes). Describe your progress, and discuss your plan for the next week. Note that this report will be graded.
Thursday, March 21th (11:59 pm): Groups submit a written progress report. Their written progress report should describe their progress to date relative to their proposed timeline, note any problems they have run into, describe your updated plan for the rest of the semester, and include any preliminary results or technical accomplishments. This written report should also include a draft of the related work section for your final paper. This report should be written in the format of a paper using the templates provided here .
Friday, April 26th: Give a 15-minute final project presentation + 5-minute Q&A in class.
Monday, May 3rd (11:59 pm): Submit a paper including an abstract, introduction (including research questions), related work, methodology, results, discussion, conclusions, and references.
Students are encouraged to submit their project as a full paper to a conference with an appropriate deadline. A paper submission will likely require additional work after the end of the semester.
| Week | Class Date | Papers | Moderator |
|---|---|---|---|
| 1 | Jan. 19 | Introduction, Planning, and Reviewing Papers
|
Shirin Nilizadeh |
| 2 | Jan. 26 | Phishing and Scam
|
Shirin Nilizadeh Elham Pourabbas Michael Aiyedun |
| 3 | Feb. 2 | Online Safety
|
Naveen Chakravarthy Koti Dhruvin Minh Tram (Jerry) |
| 4 | Feb. 9 | Project ideas
|
|
| 5 | Feb. 16 | Good Practices in Data-driven Security Research
| Uma Mahesh Sivani Tumuluri Venkata Subrahmanya Abhinav Rallapalli |
| 6 | Feb. 23 | Web
|
Minh Tram (Jerry) Venkata Subrahmanya Abhinav Rallapalli Satya Dev |
| 7 | March 1 | Online Toxicity
|
Dhruvin Shivendra Raghav Naveen Chakravarthy Koti |
| 8 | March 8 | Software Security
| Sai Bharath Vishal reddy Satya Dev |
| 9 | March 15 | Spring Break | |
| 10 | March 22 | Privacy
|
Solomon Dandekar Amirhosein Morteza Sai Bharath |
| 11 | March 29 | Machine Learning Privacy
|
Uma Mahesh Elham Pourabbas Vishal reddy |
| 12 | April 5 | DeepFakes
|
Vishal reddy Shivendra Raghav |
| 13 | April 12 | Security and Privacy of Language Models
|
Solomon Dandekar Amirhosein Morteza |
| 14 | April 19 | Machine Learning Attacks and Defenses
|
Michael Aiyedun Sivani Tumuluri |
| 15 | April 26 | Final project presentation | |
Course grades will be based on the following. This class will have no exams. Final project presentations will be held on the last day of class.
The use of Gen AI technology is prohibited for preparing the paper reviews. However, they can be utilized for projects if students indicate in the reports and presentations the extent to which, if any, Gen AI technology was used and how it was used to develop their project or reports.
You can drop 2 summaries, but at most 1 for a given week. Save these for circumstances such as falling ill or interviewing.
Students will be penalized 25% for every day it is late beyond the designated deadline. You must submit satisfactory versions of your project reports within 4 days of the deadline to pass the class. This policy will be used for the proposal, progress report, and final report.
Students may drop or swap (adding and dropping a class concurrently) classes through self-service in MyMav from the beginning of the registration period through the late registration period. After the late registration period, students must see their academic advisor to drop a class or withdraw. Undeclared students must see an advisor in the University Advising Center. Drops can continue through a point two-thirds of the way through the term or session. It is the student's responsibility to officially withdraw if they do not plan to attend after registering. Students will not be automatically dropped for non-attendance. Repayment of certain types of financial aid administered through the University may be required as the result of dropping classes or withdrawing. For more information, contact the Office of Financial Aid and Scholarships (http://wweb.uta.edu/aao/fao/).
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Students enrolled all UT Arlington courses are expected to adhere to the UT Arlington Honor Code:
I pledge, on my honor, to uphold UT Arlington’s tradition of academic integrity, a tradition that values hard work and honest effort in the pursuit of academic excellence.
I promise that I will submit only work that I personally create or contribute to group collaborations, and I will appropriately reference any work from other sources. I will follow the highest standards of integrity and uphold the spirit of the Honor Code.
UT Arlington faculty members may employ the Honor Code in their courses by having students acknowledge the honor code as part of an examination or requiring students to incorporate the honor code into any work submitted. Per UT System Regents’ Rule 50101, §2.2, suspected violations of university’s standards for academic integrity (including the Honor Code) will be referred to the Office of Student Conduct. Violators will be disciplined in accordance with University policy, which may result in the student’s suspension or expulsion from the University. Additional information is available at https://www.uta.edu/conduct/.