AI & Cybersecurity- F’21

DSCI 6015
AI & Cybersecurity
Fall 2021
Meeting Times and Location(s): MW 3:55pm – 5:10pm ET @ Buckman 233C
Credit Hours: 3
Vahid Behzadan, Ph.D.
Faculty Contact Information:
Office Location: Maxy120A or Zoom
Phone: 203-479-4723 Email: vbehzadan@newhaven.edu
Office Hours: MW 12pm-1pm or by request
Department Chair: Dr. Ali Golbazi agolbazi@newhaven.edu

COURSE SYLLABUS:
This syllabus is informational in nature and is not an express or implied contract. It is subject to
change due to unforeseen circumstances, as a result of any circumstance outside the University’s
control, or as other needs arise. If, in the University’s sole discretion, public health conditions or
any other matter affecting the health, safety, upkeep or wellbeing of our campus community or
operations requires the University to make any syllabus or course changes or move to remote
teaching, alternative assignments may be provided so that the learning objectives for the course,
as determined by the University, can still be met. The University does not guarantee that this
syllabus will not change, nor does it guarantee specific in-person, on-campus classes, activities,
opportunities, or services or any other particular format, timing, or location of education, classes,
activities, or services.

The Accessibility Resource Center can be reached at (203) 932-7332 or by email at AccessibilityResCtr@newhaven.edu. For additional information, please refer to the Accessibility Resource Center (ARC) website at www.newhaven.edu/campusaccess. For additional assistance from the Dean of Students Office, please contact: deanofstudents@newhaven.edu. If you require assistance with the technology requirement,
please visit the Student Technical Support page.

Course Description:
Prerequisite: CSCI 6602 or equivalent course in Python.

Hands-on introduction to the applications of machine learning and cybersecurity in
cybersecurity, and the security issues in AI systems. Topics covered include supervised and
unsupervised machine learning for intrusion detection, malware detection, spam classification,
and vulnerability discovery; as well as adversarial attacks on machine learning such as
poisoning, adversarial examples, and model reversal. 3 credits.
Required Text(s):
None – external resources such as reading material, slides, code, and video lectures will be
provided.

Course Structure/Course Format/Course Objectives:
This class is offered as an on-ground course, with lectures, in-class exercises, take-home  programming assignments, reading assignments, and projects. Active learning will constitute as much as 50% of the class. Participation will be recorded based on engagement in discussions (online/in-person), as well as submitted assignments.

Student Learning Outcomes:
Upon completion of this course students will be able to:

1. Practice the tools and techniques for data collection, cleaning, modeling, and visualization for
cybersecurity applications
2. Develop machine learning applications for malware detection, intrusion detection, fraud
prevention, and threat intelligence analysis for cybersecurity
3. Identify the security vulnerabilities and challenges in AI-driven applications.

Course Requirements & Assessment:
Please see official University of New Haven Academic Policies located in the links below:
Graduate Grading System

Required Text(s):
Speech and Language Processing by Dan Jurafsky and Jim Martin. 3rd Edition, (available online at https://web.stanford.edu/~jurafsky/slp3/ )

Assignments/Projects:
– All submissions are online, either via Canvas or Gradescope (as instructed in the
assignment details). Please turn in whatever you have for participation credit, event if
incomplete.
– Homework assignments can be completed via pen and paper, but the final submission must
be scanned/photographed copies of the work. If handwriting is deemed illegible there may
be a penalty, or the attempt may be completely reject.
Examinations:
– No official exam! Assessment will be based on assignments, projects, presentations, and
participation.
Participation:
Active learning will constitute as much as 50% of the class. Participation will be recorded based
on engagement in discussions (online/in-person), as well as submitted assignments.

Grading:

Grades earned are based on your performance on homework, quizzes, exams and the final exam.

Grades Scored BetweenLetter Equivalent
Class Projects25%
Quizzes/Participation10%
Paper Presentations15%
Midterm Project25%
Final Project25%
Total**100%
The calculation of final grades is determined by the faculty member. The calculated grade in the
total column in Canvas may or may not be reflective of your final grade.

**Final Grades are assigned with the following scale:

Typical Undergraduate Scale

Grades Scored BetweenLetter Equivalent
97 to 100A+
94 to Less than 97A
90 to Less than 94A-
87 to Less than 90B+
84 to Less than 87B
80 to Less than 84B-
77 to Less than 80C+
74 to Less than 77C
70 to Less than 74C-
67 to Less than 70D+
63 to Less than 67D
60 to Less than 63D-
Less than 60F
The calculation of final grades is determined by the faculty member. The calculated grade in the
total column in Canvas may or may not be reflective of your final grade.

Typical Undergraduate Scale Typical Graduate Scale
Grades Scored Between Letter Equivalent

Expectations:
Students should expect to spend at least 3 hours on academic studies outside, and in addition to,
each hour of class time. There will be readings, homework questions/problems, and programming projects.
Late Work: Assignments turned in late may be accepted with a grade penalty, if the solutions
are not distributed yet. This is completely at the discretion of the instructor, as the goal is to
balance learning and fairness.

Missed Work: Exams may be made up in only the most unavoidable situations (at the discretion
of the instructor). A formal excused absence (such as a note from Health Services or a healthcare
provider) will be required before you can make up a missed exam.
Individual Work: Students must work individually on assignments and projects unless
specifically allowed to work in groups by the instructor. Any work taken from the internet must
be cited properly (acceptance of code taken from elsewhere is at the discretion of the instructor)
or will be considered plagiarism. Failure to adhere to this policy will result in penalties ranging
from a zero on the assignment to a zero in the final grade. Students may also be subject to
disciplinary action by the University of New Haven (see University Policies).

Course Outline/Schedule:

DateTopic/Note
Week 1Intro to AI for Cybersecurity
Week 2Landscape of cybersecurity – sources of data
Week 3Introduction to machine learning – linear classifiers
Week 4SVM and Logistic Regression – Spam and Phishing Classification
Week 5 Clustering techniques for network anomaly detection
Week 6 Machine learning for malware detection I – Intro to malware analysis
Week 7 Midterm Project
Week 8 Machine learning for malware detection II
Week 9 Deep learning I – CNNs and RNNs
Week 10 Deep learning II – GANs and deep fakes
Week 11 Paper Presentations – Final Project Proposal
Week 12 Paper Presentations
Week 13Adversarial Machine Learning – Paper Presentations
Week 14 AI Safety, Security, and Ethics – Paper Presentations
Week 15 Final Project Presentations

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classes.

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