AI-Driven Cryptographic Framework for Secure Wireless Communication: A Self-Evolving Approach Against Adversarial Threats

• Minal Ghute,
• Yogesh Suryawanshi,

Vol. 19, No. 7, July 31, 2025
10.3837/tiis.2025.07.012, Download Paper (Free):

• Cryptographic Security
• Wireless Communication
• Adversarial Learning
• Federated Homomorphic Encryption
• Reinforcement Learning
• process

Abstract

The increasing reliance on wireless communication networks has escalated the need for highly secure and efficient cryptographic techniques to mitigate evolving cyber threats. Traditional encryption schemes suffer from computational inefficiencies, lack of adaptability, and vulnerability to attacks, quantum computing, and deep-learning-based decryption attacks. Although Diffie-Hellman key agreement protocols are common key exchange mechanisms, they are susceptible to MITM and quantum threats. Most existing security protocols have tried to address the problem of achieving good encryption strength with processing time limits; However, existing protocols fall short of delivering comprehensive solutions that balance encryption strength with computational efficiency sets. An ML-driven cryptographic model integrating advanced ML techniques is proposed to enhance security, efficiency, and adaptability in process. Adaptive Regularized Neural Cryptosystem (ARNC), guarantees high-entropy encryption and evolves to withstand deep-learning-based attacks. Transformer-based secure Key Exchange (TB-SKE) replaces old key exchange protocols with state-of-the-art session-key generation for quantum and adversarial threats using attention mechanisms. Therefore, Federated Homomorphic Encryption (FedHomE) allows secure computation on encrypted data to improve privacy and bandwidth efficiency. Similarly, Graph Neural Network for Adaptive Cryptographic Strength (GNN-ACS) adopts network topology analysis to modify encryption difficulty concerning real-time threats to safety, optimizing computational overhead. Lastly, Reinforcement Learning-Based Self-Evolving Encryption (RL-SEE) autonomously modifies encryption strategies to adapt continually to new cyber threats. The first results indicate that encryption performance is improved at lower processing time with various threat models. It shows an average capture of 25.2% in encryption efficiency, a maximum key exchange latency of less than 0.002 seconds, and a high level of entropy (approximately 8.0 bits per character) even under adversarial threats. In addition, the system shows fast cryptographic adaptation with an average time of 4.7 milliseconds for policy updates; thus proving its viability for practical applications in real-time wireless communication environments.

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