Reconnaissance-Strike Complex: A Network-Layer Solution to the Natural Forking in Blockchain

Anlin Chen, Shengling Wang, Hongwei Shi, Yu Guo, Xiuzhen Cheng

Abstract: The natural forking severely compromises the security and wastes resources of Blockchain. Current analyses of the natural forking are carried out from microscale and macroscale perspectives, each facing challenges in generality and accuracy respectively. This results in dire straits that the forking arising from the network layer cannot be solved within the same layer, and existing defense schemes concentrate on the consensus layer, unfortunately coming at the expense of diminishing decentralized trust. Hence, to overcome these shortcomings, we propose the first reconnaissance-strike solution to the natural forking where the issue is recognized at the network layer and further struck on-site. Specifically, our endeavors encompass 1) analyzing the spatial-temporal transmission dynamics of the main chain. We exert mesoscale perspective by transforming the behavioral analysis of the transmitters (i.e., the nodes) into the movement analysis of the transmitted object (i.e., the main chain) which mitigates following Lévy mobility. Based on this, we quantify the dynamics of long-range leaping and short-range diffusion of the main chain transmission; 2) proposing a cost-effective anti-forking mechanism. This mechanism combats the forking with low cost by configuring logical connections at the network management level, based on the quantitative relationship between Blockchain network topology and the natural forking rate we have derived. Both theoretical analysis and extensive experiments verify that our scheme can maintain the natural forking rate not more than the given threshold in most cases.

External IDs:doi:10.1109/tifs.2024.3523767