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 Between | Letter Equivalent |
Class Projects | 25% |
Quizzes/Participation | 10% |
Paper Presentations | 15% |
Midterm Project | 25% |
Final Project | 25% |
Total** | 100% |
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 Between | Letter Equivalent |
97 to 100 | A+ |
94 to Less than 97 | A |
90 to Less than 94 | A- |
87 to Less than 90 | B+ |
84 to Less than 87 | B |
80 to Less than 84 | B- |
77 to Less than 80 | C+ |
74 to Less than 77 | C |
70 to Less than 74 | C- |
67 to Less than 70 | D+ |
63 to Less than 67 | D |
60 to Less than 63 | D- |
Less than 60 | F |
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:
Date | Topic/Note |
Week 1 | Intro to AI for Cybersecurity |
Week 2 | Landscape of cybersecurity – sources of data |
Week 3 | Introduction to machine learning – linear classifiers |
Week 4 | SVM 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 13 | Adversarial Machine Learning – Paper Presentations |
Week 14 | AI Safety, Security, and Ethics – Paper Presentations |
Week 15 | Final Project Presentations |
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