burakoktenli / adara-deception-risk Public

Proactive deception prior architecture that adjusts authority downward pre-emptively based on P(adversarial). Deception Probability Engine, Phantom Fleet detection, Bayesian update, cross-sensor consistency scoring.

adaraadversarial-deceptiondeception-priorphantom-fleetbayesian
3,680 lines 181KB HTML / JS / CSS
main 2 files · Mar 14, 2026
Open Live
adara-simulation.html ADARA v10.0 Flash War latency architecture Mar 14, 2026
LICENSE All Rights Reserved · Proprietary Mar 14, 2026
adara-bundle.jsxReact component bundle (JSX source)Mar 14, 2026
adara-simulation.html 3,680 lines · 181KB 
  1<!DOCTYPE html>
  2<html lang="en">
  3<head>
  4<title>ADARA v10.0</title>
  5
  6/* ADARA: Adversarial Deception-Aware Risk Architecture
  7 * for Multi-domain Escalation Control
  8 * Author: Burak Oktenli
  9 * Georgetown University
 10 * MPS Applied Intelligence
 11 */
 12
 13// Full source: 3,680 lines
README.md

ADARA: Adversarial Deception-Aware Risk Architecture for Multi-domain Escalation Control

ADARA is the first proactive deception prior for autonomous governance. It adjusts operational authority downward pre-emptively based on the probability that current inputs are adversarially manipulated, even before manipulation is confirmed. Unlike reactive hallucination detection, ADARA computes P(adversarial) from input distribution anomalies, temporal correlation, cross-sensor consistency, and Bayesian mission history.

Patent Status

Published on Zenodo · DOI: 10.5281/zenodo.19043924.
Author: Burak Oktenli
Affiliation: Georgetown University, M.P.S. Applied Intelligence
Status: Published on Zenodo · DOI: 10.5281/zenodo.19043924, interactive simulation live.

Key Features

  • Deception Probability Engine (DPE)
  • Deception-Adjusted Authority: A_adj = A_hmaa × (1 - λ × P_deception)
  • Phantom Fleet Detection Module for AI-hallucinated hostile force scenarios
  • Bayesian update over mission history
  • Cross-sensor consistency scoring
  • Input distribution anomaly detection
  • Temporal correlation pattern analysis
  • λ deception sensitivity calibration
  • Real-time authority adjustment visualization

Technical Specifications

  • Language: HTML, JavaScript, CSS (single-file architecture)
  • Dependencies: None (zero external libraries)
  • Runtime: Browser-based, client-side only
  • Lines of code: 3,679

Author

Burak Oktenli
Georgetown University, M.P.S. Applied Intelligence
ORCID: 0009-0001-8573-1667
Contact: info@burakoktenli.com

License

Copyright © 2026 Burak Oktenli · Georgetown University M.P.S. Applied Intelligence · ORCID 0009-0001-8573-1667 · Washington, DC · CC BY 4.0