Abstract
The growing reliance on encrypted communication networks and privacy-preserving technologies, such as Tor, has intensified the demand for advanced defenses against traffic analysis attacks. Although encryption conceals content, the exposure of metadata—such as timing, packet size, and traffic volume—remains a significant vulnerability, allowing adversaries to infer private user behavior and visited websites. This dissertation addresses the limitations of existing analysis by advancing the application of deep learning techniques across multiple domains of traffic analysis and fingerprinting. The first major contribution is a comprehensive evaluation of current website fingerprinting defenses, revealing critical weaknesses against sophisticated modern attacks. Building on this foundation, a novel attack is introduced, utilizing transformer architectures and enhanced feature representations to capture complex, long-range dependencies in network traffic and significantly improve attack accuracy against defenses. Additionally, this dissertation presents an improved flow correlation attack on Tor, leveraging a transformer-based model to more effectively correlate traffic between entry and exit nodes, thus heightening the threat to user anonymity. Finally, this dissertation demonstrates an adaptation of these traffic analysis techniques for defense in the form of stepping-stone intrusion detection, addressing key challenges such as protocol variability, multi-hop complexity, and traffic obfuscation to develop more robust and adaptable detection methods in complex network environments.
Publication Date
11-2025
Document Type
Dissertation
Student Type
Graduate
Degree Name
Computing and Information Sciences (Ph.D.)
Department, Program, or Center
Computing and Information Sciences Ph.D, Department of
College
Golisano College of Computing and Information Sciences
Advisor
Matthew Wright
Advisor/Committee Member
Nicholas Hopper
Advisor/Committee Member
Nidhi Rastogi
Recommended Citation
Mathews, Nate, "Advancing Applications of Deep Learning on Network Traffic Analysis and Fingerprinting" (2025). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/12388
Campus
RIT – Main Campus
