Research on AI security enhanced encryption algorithm of autonomous IoT systems

Aiming at the security issues during the multi-types data storage and data transmission in autonomous Internet of Things (IoT) systems, this paper proposes an AI algorithm for data enhanced encryption used in the ends and the intermediate nodes of IoTs. The algorithm in this paper first constructs a three-dimensional Arnold transformation matrix for data unit value encryption in the end of IoTs, and designs a quantum logic intelligent mapping that effectively diffuses the encrypted data units to reduce the linear correlation of the image data and to improve the security performance of IoT edge data. Furthermore, the algorithm designs an AI access strategy for scrambling sequence nodes and builds a random-access route for the elements of the scrambling sequence which can reduce the calculation cost and improve the operating efficiency of IoT system in the ends and intermediate nodes. Finally, the data shared matrix is used to share the encrypted data to achieve the (k, n) threshold strategy. Experimental results prove that the algorithm has high plaintext and key sensitivity and can effectively resist brute force attacks, statistical analysis and differential attacks. The algorithm in this paper provides an AI solution for data security encryption in the ends and the intermediate nodes of autonomous IoT systems.