A Broadcast Storm Detection and Treatment Method Based on Situational Awareness

At present, the research of blockchain is very popular, but the practical application of blockchain is very few. The main reason is that the concurrency of blockchain is not enough to support application scenarios. After that, applications such as Intervalue increase the concurrency of blockchain transactions. However, due to the problems of network bandwidth and algorithm performance, there is always a broadcast storm, which affects the normal use of nodes in the whole network. However, the emergence of broadcast storms needs to rely on the node itself, which may be very slow. Even if developers debug the corresponding code, they cannot conduct an effective test in the whole network. Broadcast storm problem mainly occurs in scenarios with large transaction volume, such as the financial industry. Due to its characteristics, the concurrency of transactions in the financial industry will increase at a certain time. If there is no effective algorithm to deal with it, the broadcast storm will be triggered and the whole network will be paralyzed. To solve the problem of the broadcast storm, this paper combines blockchain, peer-to-peer network, artificial intelligence, and other technologies, and proposes a broadcast storm detection and processing method based on situation awareness. The purpose is to cut off the further spread of broadcast storms from the node itself and maintain the normal operation of the whole network nodes.