Research on Encryption Algorithm and Embedded System Optimization Strategy Based on IoT Security

In the face of increasingly complex IoT environments and increasing data volumes, existing encryption algorithms still need to be further optimized in terms of the balance between efficiency and security. For embedded systems, their hardware resources are limited, and the current optimization strategies still have shortcomings in improving system performance, reducing power consumption and enhancing system stability. In this study, the optimization strategy of encryption algorithm and embedded system based on IoT security is studied. Designed for lightweight encryption algorithms and deployed in embedded systems, it aims to balance the security and performance of IoT devices and provide users with a seamless and reliable service experience. In this study, three encryption algorithms, AES-Light, SPECK and SIMON, were selected and compared on ARM Cortex-M series microcontrollers. Experiments show that the SPECK algorithm leads with its excellent encryption and decryption rate, which is 15% faster than AES-Light, while the power consumption of SIMON is reduced by 20%. Based on this, preferred encryption schemes suitable for different IoT scenarios are established. In addition, in order to overcome the limitation of fixed encryption settings in a dynamic network environment, this project proposes an adaptive encryption strength adjustment strategy. By monitoring the risk level of the equipment in real-time and automatically optimizing the encryption parameters, the security guarantee is improved by 30% in high-risk situations while avoiding unnecessary computing overhead in low-risk scenarios, improving the overall efficiency by more than 15%, and significantly enhancing the intelligence and adaptability of IoT systems.