Adaptive Cryptographic Orchestration Against Learning-Enabled Adversaries

Abstract

Artificial intelligence (AI) has transformed the cyber threat landscape by enabling adversaries to automate reconnaissance, optimize probing strategies, and adapt attacks using feedback. While modern cryptographic primitives remain mathematically secure under standard assumptions, real-world cryptographic deployments often expose operational signals—such as timing behavior, request patterns, error responses, and negotiation outcomes—that learning-enabled adversaries can exploit to improve attack efficiency.

This paper addresses the systems-level problem of predictable cryptographic deployments enabling adversarial learning. We propose Adaptive Cryptographic Orchestration (ACO), a closed-loop defensive framework that dynamically reconfigures cryptographic and protocol parameters in response to telemetry-driven threat estimation, while preserving the use of standardized and vetted cryptographic primitives. We formalize attacker–defender interaction as a non-stationary learning environment, demonstrate why adaptive reconfiguration disrupts reinforcement-learning convergence, and present a simulation-based evaluation showing that ACO significantly reduces attacker learning efficiency under bounded operational overhead. ACO complements traditional cryptographic guarantees by reducing operational predictability in AI-accelerated attack settings.