CSE 704

Seminar: Machine Learning and Cybersecurity

Fall 2023

General Information


Dr. Hongxin Hu
E-mail: hongxinh@buffalo.edu
Homepage: https://cse.buffalo.edu/~hongxinh/
Office Hours: By Appointment
Time and Location: R 10:00 AM-11:30 PM, Davis 113A.


In this seminar class, we will discuss the use of machine learning, especially deep learning, for detecting and mitigating cyber threats arising in commercial systems and applications. We will also discuss security issues in machine learning (adversarial attacks and defenses on deep learning, backdoor attacks and defenses on deep learning, Security in Large Language Models including ChatGPT etc.). Our ability to identify the type of machine learning algorithms that are useful for specific security applications can help us improve our defenses against attacks such as credit card fraud, malware, and spam, and also anticipate the potential attack variants that may arise in the future. In addition to lectures, you'll participate in hands-on projects that will simulate a cyber threat and defense. You'll learn how to extract essential features, preprocess data and then identify a suitable suite of machine learning algorithms that can be used to detect and mitigate the cyber threat.

The main goal of the seminar is to help students understand the state of the art in a variety of topics in emerging machine learning and cybersecurity. As a secondary goal, students will learn how to read research papers and how to communicate technical material effectively.

The seminar is suitable for students who have strong interest in achine learning and cybersecurity and intent to pursue a career in the area, e.g., PhD students already working in ML and cybersecurity or MS students interested in pursuing a PhD or doing research in the field (in the form of independent studies and/or MS Thesis). One of the goals of this seminar is to identify, by the end of the semester, a set of open research problems on which students can work during the next semester, e.g., in the form of independent studies.

Tentative Schedule

Week Date Topic Papers (Presenters) Notes
1 Aug 28 Class Overview N/A  
2 Sept 4 Labor Day N/A  
2 Sept 11 Cybersecurity Overview I N/A  
3 Sept 18 Cybersecurity Overview II N/A
4 Sept 25 Network Security Overview I N/A  
5 Oct 2 Network Security Overview II N/A  
6 Oct 9 Fall Break N/A  
6 Oct 16 Adversarial Machine Learning I N/A Ke Yan
7 Oct 23 Adversarial Machine Learning II & Labs N/A Ke Yan
10 Nov 6 Topic #2: Large Language Models for Vulnerability Detection and Repair I

Review #1 (for the paper assigned to you) is due.

9 Oct 30 Topic #1: Large Language Models for Reasoning
11 Nov 13 Topic #2: Large Language Models for Vulnerability Detection and Repair II

The 1st lab is due:

  • Lab Instruction
  • Lab Colab
  • 12 Nov 20 Topic #3: Large Language Models for Security Applications
    13 Nov 27 Topic #4: Security in Large Language Models I

    Review #2 (for any paper presented by someone else) is due.

    14 Dec 4 Topic #4: Security in Large Language Models II
    15 Dec 11 Topic #5: Security in Large Vision-Language Models

    The 2nd lab is due:

  • Lab Instruction
  • Lab Colab
  • Seminar Structure and Assignments

    I will present material during the first 3 classes, followed by 2 lectures present by one of my PhD student Keyan Guo. Students then present selected papers during the remaining classes including two invited talks. A list of papers from top security and ML conferences (IEEE S&P - Oakland, USENIX Security, ACM CCS, NDSS, NeurIPS, ICML, etc.) will be provided for each topic. All students are encouraged to read all papers, submit reviews for a subset of them, and participate in discussions in class.

    The course includes the following assignments:

    Course Cridits

    Tentative Grading

    1 Credit 3 Credits Note that the class will be (and is required to be) graded pass/fail. To receive pass, you need to score 70% or more.


    Google Scholar

    ACM's Computing Research Repository

    IEEE Symposium on Security and Privacy


    USENIX Security