What question did this study set out to answer?

The research aims to present a cognitive security system that evolves defenses through real-time learning from attacks.

March 10, 20260 citationsOpen Access

The Isomer Trap: A Self-Evolving Cognitive Defence Architecture and its Implications for Post-Quantum Security

BPBiniru projects SKS. Van KrunckelsvenSecurity Information Service

Key Points

The research aims to present a cognitive security system that evolves defenses through real-time learning from attacks.
Introduced the Isomer Trap, a six-layer cognitive security architecture.
Developed an elaborate decoy environment to lure attackers.
Employed AI for real-time evolution of security measures based on attack interactions.
Validated with 1,124 attack requests across multiple attacker profiles.
Achieved zero data leaks from all attack requests.
Attained a 100% attacker profiling rate with the system.
Detected labyrinths at 88% confidence, maintaining zero escapes or unauthorized access.

Abstract

ABSTRACT This blackpaper introduces the Isomer Trap — a six-layer cognitive securityarchitecture that approaches the problem of unauthorised system access from afundamentally different angle than any existing solution. Rather than buildingstronger walls, the Isomer Trap removes the concept of a wall entirely. Anattacker — human, automated, or quantum-powered — is welcomed into a perfectlyreplicated decoy environment, guided through an elaborate sequence ofvalid-seeming steps, silently traced, and used as an unwitting trainer for anAI that evolves the defence in real time. The system does not react to attacks. It grows from them. Given sufficient training cycles, it reaches a state whereattacks are anticipated before they occur and potential attackers lose interestbefore making contact. "The best defence is one the attacker never recognises as a defence. " Description: Version 2. 0 — March 08, 2026 This release adds Technical Supplements v2. 0, which provides: • Complete solutions to all seven open research questions identified in the original Blackpaper (Q01–Q07) • Implementation architecture for secure inter-node communication, AI core protection, and adversarial training defense • Performance overhead analysis with optimal operating parameters • Legal and ethical framework for GDPR-compliant passive tracing • Working prototype validation results: 1, 124 attack requests across 8 attacker profiles (script kiddie, pentester, APT, bot swarm, insider threat, automated scanner) — zero data leaked, 100% attacker profiling rate • AI-powered adaptive attacker test results: labyrinth detected at 88% confidence, zero escape, zero real access gained Files in this version: • IsomerTrapSUPPLEMENTSᵥ2. 0. html — Seven open questions answered + prototype test results (CC BY-NC 4. 0) Previous version files (v1. 0, March 06, 2026): • IsomerTrapBLACKPAPER. pdf — Original concept document (CC BY 4. 0) • IsomerTrapSUPPLEMENTSᵥ1. 0. html — Architecture diagram, AI core spec, threat model, references (CC BY 4. 0) Note on licensing: The original Blackpaper and Supplements v1. 0 remain under CC BY 4. 0 (free for any use with attribution). Supplements v2. 0 is released under CC BY-NC 4. 0 — free to read, share, and use for non-commercial purposes. Commercial use of the implementation architecture requires written permission from the author. "We did not build a stronger lock. We made the door disappear. "

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