Energy-Efficient Cryptographic Protocols for Sustainable IoT Security: A Federated Learning-Enhanced Lightweight...

Area of Science:

• Cybersecurity
• Network Security
• Cryptography

Background:

• Resource-constrained environments in IoT/IIoT face security challenges from high computational costs.
• Quantum computing poses a future threat to current cryptographic methods.
• Existing solutions struggle with energy efficiency, security levels, and latency.

Purpose of the Study:

• To propose an Energy-Efficient Cryptographic Protocol Framework (EECPF) for sustainable IoT security.
• To achieve mutual optimization between energy consumption, security level, and communication latency.
• To address the security risks posed by quantum computing in IoT environments.

Main Methods:

• Developed an adaptive encryption selection mechanism based on device capabilities, network conditions, and threat levels.
• Integrated privacy-preserving federated learning for distributed intrusion detection and collaborative threat intelligence sharing.
• Incorporated lattice-based post-quantum cryptography and blockchain for identity management.

Main Results:

• Achieved a 94.7% intrusion detection rate.
• Reduced energy consumption by 47.3% and latency by 23.8% compared to existing lightweight methods.
• Demonstrated consistent performance across heterogeneous devices and deployment environments.

Conclusions:

• EECPF provides an energy-aware, quantum-resilient, and scalable security solution for next-generation IoT systems.
• The framework effectively balances security, energy efficiency, and performance under resource constraints.
• EECPF is suitable for critical IoT applications like smart healthcare, industrial automation, and smart cities.