Below is a list of 4080/4480 projects proposed by faculty members for Summer 2022.

Course: EECS4480

Supervisor: Jonatan Schroeder

Contact: jonatan@yorku.ca

Unit testing platforms like JUnit and Unity provide a simple interface to evaluating the correctness of individual functions in a large project. These platforms can also be used in an academic environment to automatically test student-submitted code in programming assignments and generate a grade based on if these tests pass or fail. However, given that these platforms were originally developed for running code that is expected to be trusted, this practice can lead to a potential risk if students are able to provide code that causes the test to pass without resulting in the expected value (see https://www.seas.upenn.edu/~hanbangw/blog/hack-gs/). While most modern autograding platforms introduce security practices to avoid this kind of code from receiving a valid grade, some vulnerabilities still exist.

For this project you will strengthen the security of an autograder process for C code for the PrairieLearn platform. You will start by creating possible attack vectors in the form of code that is expected to cause the autograder to pass a test without actually returning the expected results. Examples of attack vectors include code that saves or outputs well-formatted values that are interpreted by the autograder as a success, and/or crashing the original autograder process. Then you implement safeguards that ensure student-submitted code is unable to bypass container sandbox limitations, and that ensure that malicious student code does not result in a successful grade.

Required skills: Must have completed EECS 2031 with an A/A+. Must have solid programming skills in C. Ability to work independently.

Recommended skills: EECS 3221 is highly recommended. Experience with Docker containers is helpful but can be obtained during the project. Git experience is helpful.

Course: EECS4480/4080

Supervisor: Yan Shvartzshnaider

Contact: Please complete this form: https://forms.gle/oVVg6hEConSNf9p28 For any question email: yansh@yorku.ca

This project involves performing privacy assessment of online services and platforms. The student will help design usable-privacy tools that analyze information handling practices of online services.

For prior project, see work https://wiki.eecs.yorku.ca/course_archive/2021-22/F/4080_4088_4090_4480_4070/4088_presentation_schedule

Required skills: Ability to work indepently. Experience in full-stack development and using Jupyter and R notebooks for data analysis.

Recommended skills: Experience with Machine Learning, Natural Language Processing techniques, HCI design. Interest in usable privacy, critical analysis of privacy policies and privacy related regulation.

Course: 4080/4088/4090

Supervisor: Uyen T. Nguyen

Contact: utn@eecs.yorku.ca

The student will be responsible for developing a responsive web application. The primary focus will be the development of the user interface, web services, API, and database interactions while ensuring robust integration, system stability, and responsiveness among all application modules.

Responsibilities:

• Development of a modern web application with a responsive user interface, and a back-end responsible for processing user input and interacting with database and various API services
• Optimization of the application for maximum speed and scalability
• Design and implementation of data storage solutions
• Collaborate with other team members

Required Skills and Qualifications:

• Good knowledge of a back-end engineering with Python and Django
• Good understanding of front-end technologies including Javascript, HTML5 and React

Framework:

• Good knowledge of RESTFul web API concept and architecture
• Good knowledge of database systems such as SQL and NoSQL
• Good knowledge of JSON or XML file formats and methods to consume/produce them
• Ability to create database schemas that represent and support business processes

• Understanding of code versioning tools such as Git • Understanding of Job Queuing solutions such as RQ • Understanding of Docker

Good to Have:

• Understanding of designing scalable and efficient big data web applications solutions with multiple software components and background services
• Familiarity with Amazon Web Services (AWS)
• Understanding of secure coding practices and Agile software development lifecycle
• Recommended education level: 3rd year undergraduate or higher in Computer Science or equivalent program with industry experience

Notes:

• Email Prof. Uyen T. Nguyen (utn@eecs.yorku.ca) a copy of your resume listing courses, projects and prior experience relevant to the project.
• A student completing this project successfully will be hired (paid) as a research assistant for at least two more terms to continue the next step of the project.

Course: 4080/4088/4090

Supervisor: Uyen T. Nguyen

Contact: utn@eecs.yorku.ca

The student will assume the role of a System Engineer responsible for supporting the development team by designing and maintaining agreed-upon infrastructure, CI/CD pipelines, and code repositories.

Responsibilities:

• Design implement, and test agreed-upon server infrastructures
• Ensure application components are up and running and properly inter-connected
• Development and maintenance of automated deployment and CI/CI pipelines
• Maintenance of application/server monitoring tools
• Design and maintain Docker containers to support various parts of the application
• Collaborate with other team members

Required Skills and Qualifications:

• Good knowledge of GIT version control
• Good understanding of AWS services such as EC2
• Familiarity with Docker and Kubernetes
• Design and improve the CI/CD pipeline using GitLab CI
• Basic troubleshooting skills on Linux and Windows
• Troubleshoot development and production issues across multiple dev environments and operating platforms

Good to Have:

• Strong familiarity with Amazon Web Services (AWS) services
• Familiarity with any CI/CD tools such as Jenkins, Ansible, Azure DevOps
• Familiarity with deploying cloud-based big data web applications with multiple software components and background services
• Recommended education level: 3rd year undergraduate or higher in Computer Science or equivalent program with industry experience

Notes:

• Email Prof. Uyen T. Nguyen (utn@eecs.yorku.ca) a copy of your resume listing courses, projects and prior experience relevant to the project.
• A student completing this project successfully will be hired (paid) as a research assistant for at least two more terms to continue the next step of the project.

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