CSE 702

Seminar: Machine Learning and Cybersecurity

Spring 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 9:30 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 Feb 2 Class Overview N/A  
2 Feb 9 Cybersecurity Overview N/A  
3 Feb 16 Network Security Overview N/A
4 Feb 23 Deep Learning Overview N/A Ke Yan
5 March 2 Adversarial Machine Learning & Labs N/A Ke Yan
6 March 9 Machine Learning in Computer Security N/A Invited Talk from Feng Wei
7 March 16 Understanding and Defending Against Cyberharassment in the Era of Machine Learning N/A Invited Talk from Nishant Vishwamitra
8 March 23 Spring Break N/A
9 March 30 Topic #1: Adversarial Attacks and Defenses in Deep Learning
10 April 6 Topic #2: Backdoor Attacks and Defenses in Deep Learning

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

11 April 13 Topic #3: ChatGPT Safety

The 1st lab is due:

  • Lab Instruction
  • Lab Colab
  • 12 April 20 Topic #4: Security in Large Language Models and Malware Detection
    13 April 27 Topic #5: Online Abuse Defense

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

    14 May 4 Topic #6: Deep Learning-based Network Intrusion Detection

    The 2nd lab is due:

  • Lab Instruction
  • Lab Colab
  • 15 May 11 Topic #7: Deep Fake Defense  

    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