|
Lecture |
|
|
|
|
|
|
|
|
|
|
Seminars HU |
Enrollment Information (not real time - data refreshed nightly)
|
|
|
|
|
|
|
|
Class #:
|
23015 | |
Enrollment Capacity:
|
34 |
|
Section:
|
HU |
|
Enrollment Total:
|
34 |
|
Credits:
|
1.00 - 3.00 credits
|
|
Seats Available:
|
0 |
|
Dates:
|
01/30/2023 - 05/12/2023 |
|
Status:
|
CLOSED |
|
Days, Time:
|
R , 9:30 AM - 12:20 PM |
| Room: |
Davis 113A |
view map |
| Location: |
North Campus |
|
|
|
|
|
| Reserve Capacities |
|
|
| Description |
Enrollment Capacity |
Enrollment Total |
|
| Force Reg: Seats Reserved |
34 |
0 |
|
|
|
| Comments |
|
|
| Machine Learning for Cybersecurity
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, 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.
Please use the force registration system (https://academics.eng.buffalo.edu/force-registration/request) |
|
|
| Course Description |
|
|
| This course is a seminar. Seminar topics change every semester. Please refer to seminar instance topics and descriptions by semester |
|
|
| Instructor(s) |
|
|
| |
Hu |
look up |
|
|
|
|
| On-line Resources |
|
|
|
|
|
|
| Other Courses Taught By: Hu |
|
|
|
|
