Date of Award

3-2023

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Electrical and Computer Engineering

First Advisor

Barry E. Mullins, PhD

Abstract

This work investigates stacked optics detection methodologies to successfully detect and identify observational systems with a cyber-physical sensing tool, ODIN (Observational Device Identification Network). ODIN successfully detected the presence of stacked optics and LiDAR systems using night-vision devices with a 96.32% average accuracy rating, both overt and covertly placed, with objective lens diameters ranging from 17 mm to 50 mm at distances between 1 m to 5 m with and without commonly employed anti-reflective countermeasures. ODIN provides a foundation for counter- measure capabilities of NIR devices and stacked optical systems in stationary environments. Additionally, a pilot study on smartphone LiDAR emission was conducted to demonstrate an asymmetric threat capable of defeating traditional concealment TTPs. This work concludes by stressing the current vulnerabilities presented by modern smartphones which can be used as an adversarial espionage device or may cause inadvertent exposure in expeditionary environments. Lastly, research and technology recommendations are provided to defend against surveillance efforts, conduct counter- surveillance, obfuscate night vision capabilities, and reduce the risk of friendly fire incidents in the field.

AFIT Designator

AFIT-ENG-MS-23-M-053

Comments

A 12-month embargo was observed.

Approved for public release. Case number on file.

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