Adaptive Traffic Camouflage
Sep 22, 2026
·
1 min read
Adaptive Traffic Camouflage protects device identity against traffic-based fingerprinting by selecting camouflage actions using observations from previously completed traffic windows. It assesses leakage, identifies resource-feasible actions, and applies guarded action selection under bandwidth and latency constraints. The framework is evaluated across public IoT traffic datasets and multiple attacker adaptation regimes.
Authors
Daniel Adu Worae
(he/him)
Ph.D. Candidate in Computer Science and Engineering
I am a Ph.D. Candidate in Computer Science and Engineering at the University of Notre Dame. My research focuses on secure, interpretable, and privacy-preserving IoT systems, with emphasis on network traffic analysis, anomaly detection, retrieval-augmented generation, and LLM-augmented operational intelligence.