DEFENSE AND RESILIENCE OF AI SYSTEMS FOR OPTIMIZING CRYPTANALYSIS IN DEFENSE APPLICATIONS: DEVELOPING ROBUST CIPHERS THROUGH MACHINE LEARNING AND PREEMPTIVE WEAKNESS DETECTION

Аннотация

The rapid advancement of generative AI models in 2026, including GPT-4o and LLaMA 3.1, has demonstrated unprecedented capabilities in automated cryptanalysis. These models can break classical ciphers such as Vigenère 15.7 times faster than brute-force methods, posing immediate threats to defense communication systems. This paper introduces CryptoDefender, a novel adversarial training architecture that synthesizes robust block ciphers with preemptive weakness detection. Unlike traditional approaches that evaluate ciphers after design, CryptoDefender integrates a CipherGAN attack simulator during the training phase, forcing the cipher generator to evolve against adaptive neural cryptanalysis. The methodology achieves a 2.2x security margin improvement over GOST R 34.12-2015 baseline and resists 95% of AI-based cryptanalysis attacks in controlled experiments. Empirical validation includes legacy VPN protocols and IoT edge devices, demonstrating practical deployability. The paper provides pseudocode, hyperparameter configurations, and comparative resilience curves. CryptoDefender represents the first generative AI-resistant cipher synthesis framework suitable for government and military applications requiring long-term confidentiality against adaptive adversaries.