Spring 2023 - CMPT 318 D100
Special Topics in Computing Science (3)
Class Number: 6580
Delivery Method: In Person
Course Times + Location:
Mo 8:30 AM – 9:20 AM
AQ 3005, Burnaby
Th 8:30 AM – 10:20 AM
AQ 3005, Burnaby
1 778 782-6775
Prerequisites:CMPT 225 with a minimum grade of C-. Additional prerequisites to be determined by the instructor subject to approval by the undergraduate program chair.
Special topics in computing science at the 300 level. Topics that are of current interest or are not covered in regular curriculum will be offered from time to time depending on availability of faculty and student interest.
This course introduces cybersecurity (cyber security) and explores cyber situational awareness concepts and threat intelligence models. Cyber security analytics and probabilistic modeling for threat detection and response (mitigative action) will play a central role. Fundamental principles and practices of cyber security risk assessment, intrusion detection and prevention methods and their application to critical infrastructure protection will be discussed in detail. Coursework involves using the R language and software environment for statistical computing and graphics.
- Cyber threat analysis and intrusion detection
- Advanced persistent threats and zero day exploits
- Anomaly detection and scoring methods
- Stochastic processes and Markov models
- Probability and probabilistic modeling
- Time series analysis and forecasting
- Risk assessment and management
- Blockchain technology
COURSE-LEVEL EDUCATIONAL GOALS:
- Develop a general understanding of common cyber security concepts, principles and practices used in responding to the globally evolving threat landscape.
- Aquire detailed knowledge and skills for analyzing and detecting intrusions in complex industrial control systems, known as operational technology (OT), widely used for the continuous operation of critical infrastructure and the services such infrastructure provides.
- Boost awareness of challenges and opportunities arising from this rapidly advancing field of computing science.
- The course has three tests 30%
- Three graded assignments 20%
- A term project organized as group project 45%
- Active class participation 5%
There will also be reading assignments and several tutorials on using R. This grading scheme is tentative and to be finalized during the first week of classes.
MATERIALS + SUPPLIES:
Course materials (articles, lecture notes, tutorial notes, slides et cetera) will all be provided through the online course home page as PDF documents.
An Introduction to Statistical Learning with Applications in R
G. James, D. Witten, T. Hastie, and R. Tibshirani
How to Measure Anything in Cybersecurity Risk
Douglas W. Hubbard and Richard Seiersen
John Wiley & Sons
Fundamentals of Machine Learning for Predictive Data Analytics
John D. Kelleher, Brian Mac Namee, and Aoife D'Arcy
The MIT Press
REQUIRED READING NOTES:
Your personalized Course Material list, including digital and physical textbooks, are available through the SFU Bookstore website by simply entering your Computing ID at: shop.sfu.ca/course-materials/my-personalized-course-materials.
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS
SFU’s Academic Integrity website http://www.sfu.ca/students/academicintegrity.html is filled with information on what is meant by academic dishonesty, where you can find resources to help with your studies and the consequences of cheating. Check out the site for more information and videos that help explain the issues in plain English.
Each student is responsible for his or her conduct as it affects the university community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the university. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the university. http://www.sfu.ca/policies/gazette/student/s10-01.html