PMAPD: A Passive-Enhanced Multi-Level Aliased Prefix Detection Approach for IPv6 Scanning
Wei Zhang, Gang Ren, Xia Yin, Lin He · 2025
IPv6 scanning is a critical technique for network security assessment and Internet measurement. However, the prevalence of aliased prefixes significantly distorts scan results and severely interferes with target generation algorithms (TGAs) that rely on dynamic density feedback. To address the limitations of existing aliased prefix detection methods, such as insufficient accuracy and excessive overhead, this paper proposes PMAPD, a Passive-Enhanced Multi-Level Aliased Prefix Detection approach. PMAPD integrates passive analysis with active probing. The passive analysis component reuses existing scan data—primarily host responsiveness obtained at no extra cost from the main address scan, and opportunistically uses port and service information if available, to enhance detection accuracy and efficiency. The active probing component builds upon the strengths of prior active methods while incorporating optimizations to achieve a better balance between detection precision and cost. Experimental results demonstrate PMAPD’s superiority in improving the discovery of de-aliased active addresses, reducing aliased addresses in scanning results, and significantly lowering detection overhead. Across three different TGAs tested (6Tree, DET, 6Sense), PMAPD significantly outperforms the widely used traditional method MAPD: it improves the de-aliased hits by 12% to 57%, substantially reduces the aliased ratio in scan results by over 9% to 94%, and dramatically lowers detection overhead, costing only 3% to 17% of MAPD’s overhead. PMAPD offers a novel and effective aliased prefix detection solution for efficient and accurate IPv6 network scanning.