Abstract:

With strong heterogeneity and the limited computing ability of IoT nodes, this dissertation proposes a Trust Authorization Model based on detection feedback in IoT that is combined with the current trust model of IoT as well as implement storage and other tasks. By calculating and storing the cluster head node along with its strong ability to facilitate the data transmission and search for energy consumption, it prevents the local network from being limited by the computing power of the device. In terms of trust calculation, the threshold value is based on the recommendation. At the same time, the BP neural network algorithm with self-learning function is periodically detecting the interactive data stream, detecting the attack nodes, quickly implementing the response measures, and meeting the actual situation of unmanned IoT of mass devices. Simulation results show that this model has lower energy consumption than other similar models, has good coping ability for attacks such as malicious recommendation and malicious slander, and has a higher detection rate and response rate to attack nodes.