6MCBLM: Multi-scale CNN and BiLSTM-Attention Hybrid Model for IPv6 Target Generation

Rapid scanning of IPv6 addresses is crucial for understanding the network profile of IPv6. However, due to the vast address space of IPv6, achieving complete traversal scanning is difficult. Therefore, the key to scanning the IPv6 address space lies in reducing the scanning area of invalid addresses and improving the address generation capability. By analyzing the dataset of surviving IPv6 addresses, we can determine the address allocation mode and structured characteristics. This allows us to uncover the potential distribution pattern of active addresses and deduce the active address region for scanning and detection. In this paper, we introduce a novel algorithm called 6MCBLM, which is based on the attention mechanism of multi-scale CNN fused with BiLSTM structure. 6MCBLM predicts the generation of potential active IPv6 addresses by extracting spatial features of address sequences through word vectorization and multi-scale Convolutional Neural Networks. It also learns the temporal sequences of address sequences using the BiLSTM structure features. Moreover, the algorithm weights the spatial and temporal features using self-attention mechanism to capture important higher-order features. It combines different sampling strategies to sample the output. Experimental results demonstrate that 6MCBLM, compared to other IPv6 target generation algorithms, effectively integrates the multi-dimensional features of address structure and generates addresses based on learned feature information. The generated addresses exhibit higher survival and generation rates.