04/14/2023
By Danielle Fretwell

The Francis College of Engineering, Department of Electrical and Computer Engineering, invites you to attend a doctoral dissertation proposal defense by Ting-Li Huoh on “Deep Learning for Cybersecurity: Applications in Network Traffic Analysis and Malware Detection.”

Candidate Name: Ting-Li Huoh
Defense Date: Thursday, April 27, 2023
Time: 1:30 to 3:30 p.m. EDT
Location: This will be a virtual defense via Zoom. Those interested in attending should contact Ph.D. advisor (yan_luo@uml.edu) at least 24 hours prior to the defense to request access to the meeting.

Committee Members

  • Committee Chair, Advisor, Yan Luo, Ph.D., Professor, Electrical and Computer Engineering, University of Massachusetts Lowell
  • Hengyong Yu, Ph.D., Professor, Electrical and Computer Engineering, University of Massachusetts Lowell
  • Chunxiao (Tricia) Chigan, Ph.D., Professor, Electrical and Computer Engineering, University of Massachusetts Lowell
  • Tong Zhang, Ph.D., Principal Engineer, Intel Corporation
  • Peilong Li, Ph.D., Assistant Professor, Computer Science, Elizabethtown College

Brief Abstract: As computer network traffic grows, cybersecurity has become a challenge because of the complexity and dynamics of emerging network applications. The aim of this work is to deploy and develop deep learning tools and frameworks for network traffic analysis and malware intrusion detection. The research shows that graph-domain modeling of encrypted network traffic demonstrates superiority in executing multi-class classification by utilizing network raw data. It also shows that the proposed multi-input Transformer-based model exhibits superiority in performing binary classification on Windows Portable Executable (PE) for malware detection. The significance of this study includes: (i) it demonstrates the graph neural networks' (GNN) proof-of-concept for classifying network traffic flows and establishes a foundation for future graph-based studies in the field; and (ii) it provides deep learning-based solutions for classifying network traffic flows and detecting Windows PE malware files, which enables predicting network traces or malicious files as new data come to light.

All interested students and faculty members are invited to attend the defense via remote online access.