📄 Sciences Methods and Technologies
International Journal (SciMeTech)

Volume 2 · Issue 1 · 2026
ISSN: 3085-5284
Quasi-Symbiotic Domain Warfare: The Orbro Protocol A Low-Observable Defense Framework for AI-Saturated Threat Environments
Nicholette Lindsay
Pages 203–206 · FLUX|NteL, Austin, TX, USA
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Abstract
Contemporary cybersecurity doctrine is fundamentally reactive, depending on anomaly detection against a known baseline. This model is collapsing in AI-saturated environments where synthetic data constitutes the atmospheric norm and adversarial signals are architecturally indistinguishable from legitimate ones. This paper introduces Symbiotic Domain Warfare (SDW): a doctrinal framework in which defense systems engage adversarial signals through deceptive co-occupation rather than exclusion. The Quasi-Symbiotic variant is operationalized through the Orbro Protocol, a recursive, self-validating security architecture whose core engine is the Recurrent Palindromic Neural Network (RPNN). The RPNN enforces a palindromic integrity condition over a temporally symmetric hash window and introduces an Adversarial Reflection Mechanism that appears cooperative to attacking models while forcing recursive computational exhaustion. A Low-Observable infrastructure layer, the Aethernox Lattice, distributes verification compute across non-terrestrial LEO orbital nodes, minimizing the defense system's detectable signature. Finally, the Skylink Nodule provides a bio-frequency-anchored cryptographic interface, replacing static multi-factor authentication with a dynamic, non-synthesizable physiological signal key derived from cardiac rhythm variability, galvanic skin response, and neural oscillation data. Together, these components constitute a baseline defense standard for post-kinetic AI-saturated threat environments, advancing the field from Defended Networks toward Symbiotic Domain Systems.
Keywords: Symbiotic Domain Warfare, Quasi-Symbiotic Defense, Recurrent Palindromic Neural Network, Adversarial Machine Learning, Bio-Frequency Authentication, LEO Satellite Security, Low-Observable Cyber Operations, GAN-Based Cyber Threats, AI-Native Threat Detection

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SciMeTech · Volume 2, Issue 1 · 2026 · ISSN 3085-5284