CS 266 Syllabus

San Jose State University
Department of Computer Science
CS 266, Topics in Information Security, Spring 2022

Course and Contact information
- Instructor: Mark Stamp
- Office Location: MH 216
- Email: mark.stamp@sjsu.edu
- Text: for oh ate fore owe to 10-five-too
- Office hours: Tuesday & Thursday, 10:30am - 11:00am (Zoom), Friday 12:30pm - 1:30pm (Zoom and in person after Spring break), or by appointment
- Class Days/Times: Tuesday & Thursday, 9:00am - 10:15am
- Classroom: Hybrid mode (most lectures will be via Zoom, with class meetings in MH 225 for tests, presentations, etc.)
- Prerequisites: CS 166

Course Description
- Topics in information security: The course will focus on two distinct topics, namely, ransomware and adversarial attacks on machine learning models. We will discuss aspects of cryptography, malware analysis and detection, and machine learning/deep learning.
Learning Outcomes
- The focus of this course will be on a couple of advanced topics in information security. After completing this course students should have a solid understanding of ransomware, as well as knowledge of adversarial attacks on a wide variety of machine learning and deep learning models.
Textbook and Readings
- Most of the material presented in class will be drawn from the following two textbooks:
  1. Information Security: Principles and Practice, 3rd edition (https://www.wiley.com/en-us/Information+Security%3A+Principles+and+Practice%2C+3rd+Edition-p-9781119505884), by Mark Stamp
  2. The not-yet-published second edition of Machine Learning with Applications in Information Security (https://www.crcpress.com/Introduction-to-Machine-Learning-with-Applications-in-Information-Security/Stamp/p/book/9781138626782), by Mark Stamp
  Material that is not (yet) available will be provided in the form of a manuscript.
- Additional reading material:
  - A revealing introduction to hidden Markov models, at https://www.cs.sjsu.edu/~stamp/RUA/HMM.pdf
  - Boost your knowledge of AdaBoost, at https://www.cs.sjsu.edu/~stamp/RUA/ada.pdf
  - Deep thoughts on deep learning, at https://www.cs.sjsu.edu/~stamp/RUA/ann.pdf
  - Alphabet soup of deep learning topics, at https://www.cs.sjsu.edu/~stamp/RUA/alpha.pdf
- Additional relevant material:
  - PowerPoint slides for this class at http://www.cs.sjsu.edu/~stamp/CS266/ppt
  - PowerPoint slides for information security book at http://www.cs.sjsu.edu/~stamp/infosec/powerpoint
  - PowerPoint slides for machine learning book at http://www.cs.sjsu.edu/~stamp/ML/powerpoint
  - Current semester lecture videos are available at http://www.cs.sjsu.edu/~stamp/ML/lectures/CS266_Fall22/. If you are asked to login to access the videos, both the username and password are "infosec". Note: The instructor hereby gives students permission to record his lectures (audio and/or video). At least with respect to this class, your instructor has nothing to hide.
  - Class-related discussion will be posted on CampusWire at https://campuswire.com/c/G02A3CE6B/feed. You are strongly encouraged to participate by asking questions, as well as by responding to questions that other students ask. At the start of the semester, you should receive an email asking you to join this discussion group—if not, contact your instructor via email.
- For additional background information on the security-related topics, the following resources may be useful.
  - Computer Viruses and Malware, John Aycock, Springer 2006. Many of the applications we discuss are related to malware. Aycock's book is easy to read and in spite of being fairly old, it provides a good foundation for malware research.
  - Open Malware (at http://www.offensivecomputing.net/) includes a large collection of samples of live malware.
  - VX Heavens (at http://vx.netlux.org/) is a source for "hacker" type of information on viruses. Malware samples are also available.
  - Journal of Computer Virology and Hacking Techniques (at http://www.springer.com/computer/journal/11416) is a journal for malware-specific research papers. There are also several good conferences that focus on malware and/or machine learning applications in information security.
  - Recent masters project reports (at http://www.cs.sjsu.edu/~stamp/cv/mss.html#masters). Most of these projects involve applications of machine learning to malware or other topics in information security.
Course Requirements and Assignments
- SJSU classes are designed such that in order to be successful, it is expected that students will spend a minimum of forty-five hours for each unit of credit (normally three hours per unit per week), including preparing for class, participating in course activities, completing assignments, and so on. More details about student workload can be found in University Policy S12-3 at http://www.sjsu.edu/senate/docs/S12-3.pdf.
- Approximate Schedule
  - Week 1 --- Introduction and overview
  - Week 2 --- Cryptography
  - Week 3 --- Cryptography
  - Week 4 --- Cryptography
  - Week 5 --- Malware
  - Week 6 --- Malware
  - Week 7 --- Machine learning
  - Week 8 --- Machine learning
  - Week 9 --- Machine learning
  - Week 10 --- Deep Learning
  - Week 11 --- Deep Learning
  - Week 12 --- Deep Learning
  - Week 13 --- Adversarial attacks
  - Week 14 --- Adversarial attacks
  - Week 15 --- Project presentations
- Homework is due typewritten (include source code, but not executable files) by class starting time on the due date. Each assigned problem requires a solution and an explanation and work detailing how you arrived at your solution. Cite any outside sources used to solve a problem. When grading an assignment, your instructor may ask for additional information. Note that a subset of the assigned problems will typically be graded.
  
  Homework must be submitted via email before the start of class on the due date. Be sure to have an extra copy of your homework with you in class, and be prepared to discuss your solutions. Your written solutions must be in a pdf file. Submit any source code or other attachments in separate files (i.e., no code in the solution itself). You must provide enough discussion of your solution so that the grader can understand your solution, and so that the grader can be sure that you understand your solution. Put your written solution and any relevant source code in a folder named "yourlastname". Then zip your homework folder and submit the file yourlastname.zip via email to CS266.spr22@gmail.com. The subject line of your email must be of the form:
```
     CS266HMK assignmentnumber yourlastname last4digitofyourstudentnumber 
```
  The subject line must consist of the four identifiers listed. There is no space within an identifier and each identifier is separated by a space.
  - Assignment 0: Due Tuesday, February 1
    From the problem set at https://www.cs.sjsu.edu/~stamp/CS266/other/problems0.pdf, solve the following:
    Problems 1, 2, 5, 6, 12, 13
  - Assignment 1: Due Thursday, February 10
    From the problem set at https://www.cs.sjsu.edu/~stamp/CS266/other/problems2.pdf, solve the following:
    Problems 2, 4, 5, 10, 19, 21, 25
    To solve problem 10, use frequency analysis, as discussed in class.
  - Assignment 2: Due Tuesday, February 22
    Solve all of the problems in the problem set at https://www.cs.sjsu.edu/~stamp/CS266/other/homework2.pdf
    To train the HMMs, you can use your own code, or you can use the reference HMM code at http://www.cs.sjsu.edu/~stamp/RUA/HMM_ref_fast.zip
  - Assignment 3: Due Thursday, March 10
    Chapter 3, problems 2, 12, 14, 19, 20, 21, 24, 28, 31
    Chapter 4, problems 1, 2, 4, 7, 9, 12, 15, 16, 26, 31
  - Assignment 4: Due Thursday, March 17
    Chapter 5, problems 2, 4, 11, 12, 16, 17, 18, 25, 26
  - Assignment 5: Due Thursday, April 21
    Implement the SSMO algorithm as given in the SVM PowerPoint slides and test it on the following problems, where the "-1" set of feature vectors consist of X₁ = (0.5,0.5), X₂ = (1,1), X₃ = (1,3), X₄ = (2,1.5), and X₅ = (2,2), while the "+1" set of feature vectors are X₆ = (3,3), X₇ = (3,4), X₈ = (4,2), X₉ = (4,2.5), and X₁₀ = (5,5). In all cases, use the linear kernel, generate the indices (i,j), with i ≠ j, at random, let ε = 0.0001, and give the solution in the form of (λ₁, λ₂, λ₃, λ₄, λ₅, λ₆, λ₇, λ₈, λ₉, λ₁₀,b).
    1. Let C=10 and perform 100 iterations of the SSMO algorithm.
    2. Let C=10 and perform 10000 iterations of the SSMO algorithm.
    3. Let C=10000 and perform 100 iterations of the SSMO algorithm.
    4. Let C=10000 and perform 10000 iterations of the SSMO algorithm.
  - Assignment 6: Due Tuesday, May 17
    From the new reader that is available at Maple Press, solve the following problems:
    Chapter 9, problems 9.1 a), 9.3, 9.6 a) and b), 9.8, 9.10, and 9.13
    Chapter 10, problems 10.4 (any 4 parts), 10.5, 10.8, 10.10, and 10.15
  - Assignment 7: Due varies
    You are required to attend at least one of the Master's defense talks listed at http://www.cs.sjsu.edu/faculty/stamp/defenses/spring22.html You can attend a second defense for extra credit (actually, you can attend as many as you want, but more than two will not yield extra extra credit).
  - Assignment 8: Due TBD
  - Assignment 9: Due TBD
  - Assignment 10: Due TBD
- NOTE that University policy F69-24 at http://www.sjsu.edu/senate/docs/F69-24.pdf states that "Students should attend all meetings of their classes, not only because they are responsible for material discussed therein, but because active participation is frequently essential to ensure maximum benefit for all members of the class. Attendance per se shall not be used as a criterion for grading."
Grading Policy
- Test 1, 100 points. Date: Thursday, March 24.
- Homework, quizzes, class participation and other work as assigned and graded, 100 points. As noted above, a subset of the assigned problems will be graded.
- CS266 Project, 100 points. You must obtain approval for your project proposal from the instructor (via email) by the close of business on TBD, and you must be prepared to give a brief presentation of your proposed topic on TBD. A written project report is due Tuesday, May 10 and project presentations will begin on the same day.
- Final, 100 points. Date: Monday, May 23 from 7:15-9:30 AM. The official finals schedule is here: https://www.sjsu.edu/classes/final-exam-schedule/spring-2022.php
- Semester grade will be computed as a weighted average of the major scores listed above.
- No make-up tests or quizzes will be given and no late homework or project (or other work) will be accepted.
- Grading Scale:
  
  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-
  68 - 69 D+
  62 - 67 D
  60 - 61 D-
  59 and below F
- Note that "All students have the right, within a reasonable time, to know their academic scores, to review their grade-dependent work, and to be provided with explanations for the determination of their course grades." See University Policy F13-1 at http://www.sjsu.edu/senate/docs/F13-1.pdf for more details.
Guest Lectures
- TBD
  - Date: TBD
  - Time: TBD
  - Location: TBD
  - Title: TBD
  - Abstract: TBD
  - Short Bio: TBD
- TBD
  - Date: TBD
  - Time: TBD
  - Location: TBD
  - Title: TBD
  - Abstract: TBD
  - Short Bio: TBD
Classroom Protocol
- Keys to success: Do the homework, complete a good project, and attend class
- Wireless laptop is required. Your laptop must remain closed (preferably in your backpack and, in any case, not on your desk) until your instructor informs you that it is needed for a particular activity
- Cheating will not be tolerated, but working together is encouraged
- Student must be respectful of the instructor and other students. For example,
  - No disruptive or annoying talking
  - Turn off cell phones
  - Class begins on time
  - Class is not over until your instructor says it is over
- Valid picture ID required at all times
- The last day to drop without a "W" grade is Monday, February 7, and the last day to add is Monday, February 14
University Policies
- Office of Graduate and Undergraduate Programs maintains university-wide policy information relevant to all courses, such as academic integrity, accommodations, etc. You may find all syllabus related University Policies and resources information listed on GUP’s Syllabus Information web page at http://www.sjsu.edu/gup/syllabusinfo/

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-
68 - 69	D+
62 - 67	D
60 - 61	D-
59 and below	F