Instructor: |
Melody Moh |
Office Location: |
MQH 411 |
Telephone: |
(408) 9245088 |
Email: |
MyFirstName
<dot> MyLastName
<at> SJSU <dot> EDU |
Office Hours: |
Mon and Wed 1120 to 1150 and Wed 1330 to 1400 |
Class Days/Time: |
MW 1200 to 1315 |
Classroom: |
MQH 422 |
Prerequisites: |
CS 166, CS 265, or
instructor consent
|
Course materials such as syllabus, handouts,
notes, assignment instructions, etc. can be found on my faculty web page
http://www.cs.sjsu.edu/~melody/index.html
You are responsible for regularly checking with
the email system through MySJSU at http://my.sjsu.edu to learn
of any updates.
Advanced
topics in the area of information security. Content differs with each offering.
Possible topics include, but are not restricted to: Network Security, Software
Reverse Engineering and Cryptanalysis. Prerequisite: CS 166 or instructor
consent. Repeatable for credit when topic changes. This semester, the topics
will center around the applications of machine learning and deep learning in
information security.
Upon successful completion of this course,
students will be able to:
Required Textbooks
·
Proceedings
of the 10th ACM Workshop on Artificial Intelligence and Security, ACM, Oct 2017.
o
https://dl.acm.org/citation.cfm?id=3128572
·
Proceedings
of the 11th ACM Workshop on Artificial Intelligence and Security, ACM, Oct 2018.
o
https://dl.acm.org/citation.cfm?id=3270101
Optional Textbooks and References
· P. Tan, M. Steinbach, a. Karpatne, and V. Kumar, Introduction to Data Mining, 2nd ed., Pearson, 2018.
· William Stallings, "Cryptography and
Network Security: Principles and Practice," 7th Edition, Pearson, 2017.
· Mark Stamp, “Information Security: Principles
and Practice,” 2nd Edition, Wiley, 2011.
· Other references for specific topics/projects will be
provided along with those topic/project assignments.
Homework is due (hard copy) by class starting
time on the due date. Each assigned problem requires a solution and an
explanation (or work) detailing how you arrived at your solution. Cite any
outside sources used to solve a problem. When grading an assignment, I may ask
for additional information. A subset of the assigned problems will typically be
graded.
ASSIGNMENTS
Refer the course website for latest information
of assignments.
Success
in this course is based on the expectation that students will spend, for each
unit of credit, a minimum of 45 hours over the length of the course (normally
three hours per unit per week) for instruction, preparation/studying, or course
related activities, including but not limited to internships, labs, and
clinical practica. Other course structures will have
equivalent workload expectations as described in the syllabus.
EXAMS
One mid-term exam (Mid) scheduled
approximately at the end of 8th week, and a final exam (FIN).
For continual updates of course schedule, please
check the course
schedule webpage available at http://www.cs.sjsu.edu/faculty/melody/266_19S_GS.html
CS 266 final exam is scheduled on Friday May
17, 0945-1200. Refer to the Spring semester final exam
schedule, posted at http://info.sjsu.edu/static/catalog/final-exam-schedule-spring.html
Percentage |
Grade |
92 and above |
A |
90 - 91 |
A- |
88 - 89 |
B+ |
82 - 87 |
B |
80 - 81 |
B- |
78 - 79 |
C+ |
72 - 77 |
C |
70 - 71 |
C- |
60 - 69 |
D |
59 and below |
F |
o
HQP - 20%, PROJ- 40%, Mid - 20%, FIN - 20%.
NOTE that University
policy F69-24 at http://www.sjsu.edu/senate/docs/F69-24.pdf states
the following:
Per University Policy S16-9, university-wide
policy information relevant to all courses, such as academic integrity,
accommodations, etc. will be available on Office of Graduate and Undergraduate
Programs' Syllabus Information
web page at http://www.sjsu.edu/gup/syllabusinfo/
The schedule is subject to change with fair
notice; the notice will be made available in class.
Weeks |
Topics |
1 |
Introduction to advance information security |
2 |
Botnet detection in IoT networks |
3 |
Anomaly detection |
4 |
Log analysis |
5 |
AI for detecting attacks |
6 |
CAPTCHA: attacks and countermeasures with AI |
7 |
Deep learning |
8 |
Adversarial attacks and defenses of deep
learning models |
9 |
Authentication |
10 |
Intrusion detection |
11 |
Poisoning |
12 |
Defense against poisoning |
13 |
Malware |
14 |
Malware analysis |
15 |
Case studies |
16 |
Review |
Final Exam |
9:45am on Friday May 17. |