6Vision: Image-Encoding-Based IPv6 Target Generation in Few-Seed Scenarios
Wenjian Zhang, Guanglei Song, Lin He, Jinlei Lin, Songyun Wu, Zhiliang Wang, Chenglong Li, Jiahai Yang · 2024
Efficient global Internet scanning is crucial for network measurement and security analysis. While existing target generation algorithms verify remarkable performance in largescale detection, their efficiency notably diminishes in few-seed scenarios. This decline is primarily attributed to the intricate configuration rules and sampling bias of seed addresses. Moreover, instances where BGP prefixes have few seed addresses are widespread, constituting$63.65 \%$of occurrences. We introduce 6 Vision to tackle this challenge by introducing a novel approach to encoding IPv6 addresses into images, facilitating comprehensive analysis of intricate configuration rules. Through feature stitching, 6 Vision not only improves the learnable features but also amalgamates addresses associated with configuration patterns for enhanced learning. Moreover, it integrates an environmental feedback mechanism to refine model parameters based on identified active addresses, thereby alleviating the sampling bias inherent in seed addresses. As a result, 6Vision achieves high-accuracy detection even in few-seed scenarios. The HitRate of 6 Vision is improved by$181 \% \sim 2,490 \%$compared to existing algorithms, while the CoverNum is$1.18 \sim 11.20$times that of them. Additionally, 6Vision can function as a preliminary detection module for existing algorithms, yielding a conversion gain (CG) ranging from$242 \% \sim 2,081 \%$. Ultimately, we achieve a conversion rate (CR) of$28.97 \%$for few-seed scenarios. We enrich the IPv6 hitlist, not only enhancing current target generation algorithms for large-scale address detection in few-seed scenarios but also effectively supporting IPv6 network measurement and security analysis.