An Adaptive Hybrid Cryptographic Framework for Secure and Energy-Efficient IoT Systems: Architecture and Evaluation

Abstract

The Internet of Things (IoT) has become a fundamental component of modern cyber–physical systems; however, securing IoT deployments remains challenging due to strict constraints on computation, memory, and energy resources. Conventional cryptographic mechanisms provide strong security guarantees but often introduce excessive overhead for low-power embedded devices, whereas lightweight cryptographic solutions may reduce security robustness under dynamic operating conditions. This paper presents an adaptive hybrid cryptographic framework for secure and energyefficient IoT systems. The proposed framework combines lightweight cryptographic primitives, hybrid key establishment, and machine-learning-assisted context-aware security selection within a unified adaptive architecture. Cryptographic configurations are dynamically selected according to runtime resource availability, data sensitivity, and inferred threat conditions while preserving bounded adaptation overhead and lightweight deployment characteristics. Experimental evaluation across representative 8-bit, 16-bit, and 32-bit IoT platforms demonstrates that the proposed framework reduces energy consumption and execution latency by approximately 15–25% compared with static cryptographic configurations while maintaining strong security guarantees and improved operational scalability. Additional adversarial validation further demonstrates resistance against downgrade manipulation, replayed context signals, oscillation-triggering attacks, and unauthorized reconfiguration attempts. The results indicate that adaptive hybrid cryptographic selection provides a practical balance between security robustness, runtime efficiency, and deployment scalability for heterogeneous IoT environments.