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

Active development.
Author: Burak Oktenli
Affiliation: Georgetown University, M.P.S. Applied Intelligence
Status: Active development, 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. All Rights Reserved. This software is proprietary and protected by pending U.S. patent applications. Viewing and personal evaluation permitted. Commercial use, modification, and redistribution prohibited without written consent.