Date Approved

8-17-2026

Graduate Degree Type

Thesis

Degree Name

Applied Computer Science (M.S.)

Degree Program

College of Computing

First Advisor

Andrew Kalafut

Second Advisor

Samah Mansour

Third Advisor

Suhila Sawesi

Academic Year

2025/2026

Abstract

This thesis presents the design, implementation, and evaluation of a lightweight end-to-end cryptographic framework integrated with a semantic quality-of-service classification system for augmented reality based telesurgery. Telesurgery can deliver expert surgical care to underserved populations, but adoption has been limited by unresolved cybersecurity, network performance, and resilience challenges. The core tension is that strong encryption adds latency that may exceed the clinical safety threshold, while unencrypted systems remain vulnerable to attacks that could endanger patients during live procedures.

The framework addresses this tension through a dual-edge security middlebox that performs per-flow encryption using semantically selected ciphers: AES-128-GCM for latency-critical haptic and control flows, AES-256-GCM for video against harvest-now-decrypt-later threats, and Ascon-128a for constrained medical sensors. Session establishment uses a hybrid post-quantum key exchange (X25519 with ML-KEM-768) at under 1 millisecond per session.

Validated on a Mininet testbed across domestic, transatlantic, and transpacific conditions, the framework was tested against replay, man-in-the-middle, and volumetric denial-of-service attacks, comparing multiple defense techniques for each. Full AEAD achieved 100% detection of replay and tampering attempts, and layered network defense reduced flood-attack latency by 94%. All three research questions are answered affirmatively, confirming that clinically safe, cryptographically secured telesurgery is feasible across intercontinental distances.

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