This course provides a comprehensive overview of advanced topics in cybersecurity. Students will get an opportunity to engage with published research through reading and discussion. The course will cover both classic papers and recent results in systems security, network security, applied cryptography, and privacy. Concurrently with reading and evaluating research, groups of students will work on a novel cybersecurity research project on a topic of their choosing. The course will culminate in a research symposium, with groups presenting their findings to the class. (3 credits)
🔬 EE
G7703 (EE
59903 for undergraduates) meets Tuesdays from 5:00 - 7:45p
(Classroom TBD). |
Prerequisites: Knowledge of systems security, network security, applied
cryptography, and/or privacy as covered in by an introductory course in one or more of these areas, e.g.,
EE 59889 Cybersecurity Operations or
CSC 38000 Computer Security or CSC 48000 Cryptography.
Course instructor: Tushar Jois (Office hours: Tuesdays from 2:00 - 3:00p in Steinman 638)
Course text: None (zero textbook cost). We will be using publicly available materials.
The course schedule is available on Brightspace, and is subject to change at any time. The course staff will notify students of any schedule changes as they occur. Assignment submission and grades will also be on Brightspace.
Paper discussion sessions. The core of the class will consist of student paper presentations with a full-class discussion on the paper afterwards. The following assignments are associated with paper discussion sessions:
Term project. The ultimate goal of the class is for students conduct a novel research project in cybersecurity. The following assignments are associated with the term project:
The course will be weighted as follows:
| 30% | Term project write-up |
| 20% | Class participation |
| 20% | Paper reviews |
| 20% | Paper presentation(s) |
| 10% | Term project presentation |
This course consists of combined graduate (EE
G7703) and undergraduate (EE
59903) sections. Coursework expectations will be higher across for both paper discussion sessions and the term project for
students enrolled in the graduate section. More information on specific requirements in each section will be
provided during class.
For the graduate section (EE
G7703), the following grade
scales will apply to weighted scores, at a minimum:
| 100%: A+ | 99-92%: A | 91-90%: A- |
| 89-88%: B+ | 87-82%: B | 81-80%: B- |
| 79-78%: C+ | 77-72%: C | < 72%: F |
For the undergraduate section (EE
59903), the following grade
scales will apply to weighted scores, at a minimum:
| 100%: A+ | 99-92%: A | 91-90%: A- |
| 89-88%: B+ | 87-82%: B | 81-80%: B- |
| 79-78%: C+ | 77-72%: C | 71-70%: C- |
| 69-68%: D | < 67%: F |
The instructor may choose to curve all class grades up at the end of the course, and the above cutoffs could shift, which might improve your final grade. Note that this is not guaranteed, and would occur at the instructor's sole discretion.
Grades in the course are not subject to negotiation. If you suspect a clear grading error has been made, please consult the college's appeals process.
As provided by college policy, note that 4 or more absences from class sessions will result an automatic WU grade for the semester. Being more than 15 minutes late to class twice will count as one absence for this purpose. It is the responsibility of the student if they arrive late to check in with the instructor for attendance. This policy is intended to encourage attendance, as group discussion is a core part of this course; not being there not only hurts your educational experience, but also those of your groupmates. If you are unable to keep up with the course, or expect to miss class due to extenuating circumstances, please inform the course staff as soon as possible.
Extra credit will be awarded to the class participation category for each attendance at an EE Department Seminar. Details on this opportunity will be posted later.
This course will include topics related computer security and privacy. As part of this investigation we may cover technologies whose abuse could infringe on the rights of others. As computer scientists and engineers, we rely on the ethical use of these technologies. Unethical use includes circumvention of an existing security or privacy mechanism for any purpose, or the dissemination, promotion, or exploitation of vulnerabilities of these services. Any activity outside the letter or spirit of these guidelines will be reported to the proper authorities and may result in dismissal from the class and possibly more severe academic and legal sanctions.
Acting lawfully and ethically is your responsibility. Carefully read the Computer Fraud and Abuse Act (CFAA), a federal statute that broadly criminalizes computer intrusion. This is one of several laws that govern "hacking." Understand what the law prohibits. If in doubt, we can refer you to an attorney.
In addition to the law, as members of the City College of New York and users of its computer systems, you are also bound by its policies on computer use.
Academic dishonesty is prohibited in The City University of New York. Penalties for academic dishonesty include academic sanctions, such as failing or otherwise reduced grades, and/or disciplinary sanctions, including suspension or expulsion.
Academic integrity is at the core of a college or university education. Faculty assign essays, exams, quizzes, projects, and so on both to extend the learning done in the classroom and as a means of assessing that learning. When students violate the academic integrity policy (i.e., “cheat”), they are committing an act of theft that can cause real harm to themselves and others including, but not limited to, their classmates, their faculty, and the caregivers who may be funding their education. Academic dishonesty confers an unfair advantage over others, which undermines educational equity and fairness. Students who cheat place their college's accreditation and their own future prospects in jeopardy.
Note on Generative AI: This course assumes that all coursework and communications (including emails to the professor) have been created by the student or the student's group. The use of generative AI tools (such as ChatGPT, Copilot, Gemini, and others) to complete this course is strictly prohibited, and will be treated as academic dishonesty. Please contact the instructor if you have questions.
If you are struggling with anxiety, stress, depression, or other mental health-related concerns, please consider visiting the CCNY Counseling Center. If you are concerned about a friend, please encourage that person to seek out their services.
You are welcome to bring a family member to class on occasional days when your responsibilities require it (for example, if emergency childcare is unavailable, or for the health needs of a relative). Please be sensitive to the classroom environment, and if your family member becomes uncomfortably disruptive, you may leave the classroom and return as needed.
If you ever have concerns in this course about harassment, discrimination, or any unequal treatment, or if you seek accommodations or resources, I invite you to share directly with me, the department, or university administration. We promise that we will take your communication seriously and seek mutually acceptable resolutions and accommodations. Reporting will never impact your course grade. In handling reports, people will protect your privacy as much as possible, but faculty and staff are required to officially report information for some cases (e.g. sexual harassment).