Assist with the project "Classification of Encrypted Web Traffic Using Deep Learning for Enhanced Web Filtering."  Web filtering is a key element in cybersecurity, designed to block access to malicious sites, prevent malware, and safeguard sensitive information. By ensuring a secure and controlled online environment, web filtering solutions contribute to organizational security, employee productivity, and appropriate content access across sectors. The University of Michigan-Flint, in collaboration with Netstar Inc.--a leading Japanese URL filtering provider--is working on a machine learning approach to web filtering. This project is driven by the Secure Modeling and Intelligent Learning in Engineering System (SMILES) Lab, with a team of PhD students, postdoctoral researchers, and Netstar professionals.

• Prepare datasets
• Develop deep learning models for classifying encrypted DNS traffic

• Must be a current University of Michigan-Flint undergraduate student majoring in a related field or recently graduated from a related undergraduate program and not currently enrolled in a graduate program
• Solid understanding of computer networks
• Strong problem-solving and programming skills
• Good understanding of machine learning, computer network protocols and cybersecurity

The position will begin January 7, 2025.

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.

Skills and knowledge you will gain:

• Hands-on experience in developing deep leaning solutions for cybersecurity
• Exposure to knowledge graph-based AI applications
• Skills in model optimization for complex problems
• Presentation development for industry-level projects

University of Michigan-Flint - Plan for Diversity, Equity and Inclusion

The University of Michigan-Flint's DEI plan can be found at: https://www.umflint.edu/dei/? 

The University of Michigan-Flint exhibits its commitment to diversity, equity, and inclusion through enacting fair practices, policies, and procedures particularly in support of the equitable participation of the historically underserved. UM-Flint recognizes the value of diversity in our efforts to provide equitable access and opportunities to all regardless of individual identities in support of a climate where everyone feels a sense of belonging, community, and agency.

Diversity is a core value at University of Michigan-Flint. We are passionate about building and sustaining an inclusive and equitable working and learning environment for all students, staff, and faculty. The University of Michigan-Flint seeks to recruit and retain a diverse workforce as a reflection of our commitment to serve the diverse people of Michigan, to maintain the excellence of the University, and to offer our students richly varied disciplines, perspectives, and ways of knowing and learning for the purpose of becoming global citizens in a connected world.

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.

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.

The University of Michigan is an equal opportunity/affirmative action employer.