A large-scale ipv6 network node detection method based on self-learning algorithm

Abstract

This invention discloses a large-scale IPv6 network node detection method based on a self-learning algorithm, relating to the field of IPv6 address detection technology. The method includes: S1, initiating the detection process using an initial seed address set; S2, constructing a lightweight address generation model based on the verified initial seed address set, and generating a candidate IPv6 address set; S3, performing concurrent liveness detection on the candidate IPv6 address set; S4, collecting detection response data; and S5, inputting the detection response data into the lightweight address generation model, and performing online training and parameter updates on the lightweight address generation model. This invention achieves high hit rate and low redundancy in IPv6 address detection by automatically learning the structural characteristics of active addresses through a multilayer perceptron structure; effectively solves the detection efficiency bottleneck problem through concurrent detection and dynamic rate control strategies; and enhances the system's adaptive capability by introducing multi-dimensional feedback signals and a refined loss function.

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