CS 4301 - 0U3 Machine Learning in Cyber Security

Summer 2020

Course Information

Location: JSOM 2.717
Time: Tuesday & Thursday 10:00AM - 12:15PM
Instructor: Wei Yang
Email: wei.yang@utdallas.edu
Office: ECSS 4.225
Office Hours: By Appointment
Email: TBD
Office: TBD
Office Hours: TBD

Course Style

This course is taught in both a seminar and a regular-course style. Each student will be expected to:


Dive into Deep Learning
Building Intelligent Systems: A Guide to Machine Learning Engineering (using UTD email to access)
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (using UTD email to access)

Course Project Topic and Grading Policy

Topic: Propose your own projects (Feel free to talk to the instructor about the proposed topic). A list of topics will be provided in the class.
Grading Policy:

Schedule and Lecture Slides

Week Dates Topic
1.1 May. 26th Course Content and Course Project [Slides] [Reading]
1-5 Machine Learning for Security
1.2 May. 28th Security Analysis Foundation [Slides] [Reading1] [Reading2]
2.1 June. 2nd Testing [Slides] [Reading1] [Reading2]
2.2 June. 4th Fuzzing [Slides] [Video] [Reading1] [Reading2] [Reading3] [Reading4]
3.1 June. 9th Machine Learning Basics[Slides] [Video] [Reading]
3.2 June. 11th Automated Testing[Slides] [Reading1] [Reading2]
4.1 June. 16th Automated Testing[Slides] [Reading1] [Reading2]
4.2 June. 18th Symbolic Execution [Slides] [Reading1] [Reading2]
5.1 June. 23rd ML for Code Analysis [Presentation1] [Reading1] [Reading2]
5.2 June. 25th Analysis for ML Software [Presentation1] [Presentation2] [Reading]
TBD Reinforcement Learning & Imitation Learning [Slides] [Reading]
6.1 June. 30th Symbolic Execution (cont.) [Slides] [Presentation1] [Reading1] [Reading2]
TBD Statistical Debugging & Web Vulnerability [Slides] [Reading]
6.2 July. 2nd Embedding [Slides] [Reading]
7-10 Security for Machine Learning
7.1 July. 7th Adversarial Machine Learning-Evasion Attacks [Slides] [Presentation1]
7.2 July. 9th Adversarial Machine Learning-Other Attacks [Slides] [Reading]
7.1 July. 7th Uncertainty of Machine Learning [Slides] [Reading] [Presentation1]
6.2 July. 2nd Interpretability of Machine Learning [Slides] [Reading]
7.2 July. 9th Poisoning Attack [Slides] [Reading]
8.1 July 14th Privacy of Machine Learning [Slides] [Reading]
8.2 July 16th Fairness of Machine Learning [Slides] [Reading]
9.1 July 21st Testing of Machine Learning Models [Slides] [Reading]
9.2 July 23rd Debugging of Machine Learning Models [Slides] [Reading]
10.1 July 28th Detecting Issues in Deep Learning Applications [Slides] [Reading]
10.2 July 30th Trending Security Topics [Slides] [Reading]
11.1 August 4th Project Presentation [Slides] [Reading]