An Adaptive Hybrid Cryptographic Framework for Resource-Constrained IoT Devices

Recently, the record-level rise in Internet of Things (IoT) devices has produced unparalleled security challenges, particularly for resource-constrained devices operating under limited computational resources, memory, and power. In this context, traditional cryptographic methods not only fail but are also expensive and require extensive resources, given their static nature. In this article, an Adaptive Hybrid Cryptographic Framework (AHCF) is proposed to address the security challenges of resource-constrained IoT devices by adaptively balancing performance and protection levels, which can adaptively adjust cryptographic parameters based on the state of the device at a given time under a specific network environment and security needs. It also effectively balances security level and resource usage and employs low-overhead asymmetric key management with lightweight symmetric cryptography and machine learning-based predictors for the optimal selection of encryption schemes. Experimental testing on multiple IoT platforms has demonstrated its significant benefits, namely 42% less energy consumption, a 38% increase in processor speed, and improved security responsiveness over static deployments. This solution can be applied on boards with as little as 2 KB RAM and 16 KB flash and outperforms existing IoT standards and protocols.