Method and device for detecting ipv6 active addresses based on reinforcement learning

Abstract

This application proposes a method and device for detecting IPv6 active addresses based on reinforcement learning. The method includes: obtaining IPv6 seed addresses and determining multiple high-density areas of the seed addresses; Performing iterative detection in the density area, including: generating a preset number of target addresses in each high-density area, and detecting whether each of the target addresses is an active address; determining the number of active addresses in the preset number of target addresses and the number of inactive addresses, and update the expected reward of the corresponding high-density area according to the number of active addresses and the number of inactive addresses, and repeat the above steps to make the density of the seed address The distribution converges to a density distribution of active addresses. The method moves the density distribution of seed addresses to the actual distribution of active addresses, so that the high-density area of ​​active addresses can be determined in the network, and the efficiency of detecting active addresses is improved.

Description

technical field [0001] The present application relates to the technical field of computer networks, in particular to a method and device for detecting IPv6 active addresses based on reinforcement learning. Background technique [0002] At present, when detecting active IPv6 addresses in the target area, if there are enough seed addresses in the target area, the target address generation algorithm based on the seed addresses can usually be used to detect active IPv6 addresses. Assuming that the seed sampling distribution of active addresses in the target area is uniform, the density distribution of seed addresses is consistent with the density distribution of actual active addresses in this area. The higher the density of seed addresses in the target area, the higher the probability of detecting active addresses. Therefore, we expect to find the high-density area of ​​the seed address and perform address detection in the high-density area, so as to achieve the purpose of dete...

AI Technical Summary

Problems solved by technology

[0003] However, due to the sampling deviation of seed addresses and other factors, the density distribution of seed addresses may not be consistent with the density distribution of actual active addresses in the target area, resulting in the address detection method in the related art. The density of many actual active addresses is low address detection in the area, which reduces the detection efficiency and wastes detection resources

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