Security Collaborative Optimization Strategy for Perception Layer in Cognitive IoT

Abstract

Internet of things (IoT) has been widely used in various fields because of the high intelligence and diversity. IoT security features sensory information, network environments, and user requirements. Based on multilayer multidimensional characteristics of IoT security elements and security-oriented system indexes, this paper adopted an autonomic idea of ＂cluster user＂ collaboration to incorporate the concept of autonomic discipline to fine-tune ＂single user＂ processes and improve overall security performance in multilayer security elements. That is, from microscopic to perception layer, from singular to plural, autonomic configurations and adjustments were implemented, which achieved self-renewal and optimization of the overall system security. A multidimensional constrained optimization method was adopted to realize perception layer configuration optimization. During the process, the configuration requirements were fed back to cluster users in each layer; accordingly, security configuration optimization was triggered from the perception layer perspective. In this way, self-configuration and the ability to self-adjust were improved. The simulation results showed that without affecting its general significance (randomly assigned weight), the presented method controlled the optimization cost in a small range [3.3181, 6.96626] while at the same time it was able to optimize the perception layer security performance by more than 39%.