Job Summary
The goal of this project is to develop virtualized lab modules using docker containers on top of the existing Cyber lab exercises from the Naval Postgraduate School (Labtainers) that will make use of various machine learning algorithms for attack vectors and vulnerabilities, such as intrusion detection, malware analysis, spam filters, anomaly detection, etc. Supervised, unsupervised, and semi-supervised algorithms as well as clustering, classification, regression, time-series analysis, and deep learning methodologies will be employed from cybersecurity papers published in journal and conferences with real and synthetic data sets. Each lab module will provide step-by-step walk-through for the student as well as end-of-exercise programming exercises. The use of current problem formulations from ongoing real research will likely result in student-led submissions to peer-reviewed publications, as both PIs have significant experience in getting students involved in published research. The labs will be shared with other institutions per the target funding agency requirements.
Responsibilities*
- Literature survey
- Reproducing existing labs from the literature in Virtual Machines and dockers containers
- Work on developing a new lab module
Required Qualifications*
- Must be a current University of Michigan-Flint undergraduate student in good academic standing or recently graduated from a UM-Flint undergraduate program and not currently enrolled in a graduate program
- Good programming background with successful completion of at least CSC 175 Problem Solving and Programming I and CSC 275 Problem Solving and Programming II
Desired Qualifications*
Completion of CSC 375 Data Structures and CSC 379 Algorithm Analysis is preferred.
Work Schedule
Work on this project will take place during the Fall 2025 and Winter 2026 semester.
Modes of Work
Hybrid
The work requirements allow both onsite and offsite work and an employee has an expected recurring onsite presence. On occasion, the employee may be required and must be available to work onsite more frequently if necessitated by unit leadership or their designee and/or the job requirements.
Positions that are eligible for hybrid or mobile/remote work mode are at the discretion of the hiring department. Work agreements are reviewed annually at a minimum and are subject to change at any time, and for any reason, throughout the course of employment. Learn more about the work modes.
Background Screening
The University of Michigan conducts background checks on all job candidates upon acceptance of a contingent offer and may use a third party administrator to conduct background checks. Background checks are performed in compliance with the Fair Credit Reporting Act.
Application Deadline
Job openings are posted for a minimum of three calendar days. The review and selection process may begin as early as the fourth day after posting. This opening may be removed from posting boards and filled anytime after the minimum posting period has ended.
U-M EEO Statement
The University of Michigan is an equal employment opportunity employer.