Bibliographic Details
| Title: |
Efficient router fingerprinting in IPv6 networks. |
| Authors: |
Yang, Yifan1 (AUTHOR), Hu, Ling1 (AUTHOR), Yang, Tao1 (AUTHOR), Li, Xionglve1 (AUTHOR), Hou, Bingnan1 (AUTHOR), Cai, Zhiping1 (AUTHOR) |
| Source: |
Computer Journal. Jun2026, Vol. 69 Issue 6, p963-974. 12p. |
| Subjects: |
Internet protocol version 6, Computer network security, Routing algorithms, Computer network architectures |
| Abstract: |
The pervasive interconnection of heterogeneous routing devices forms the fundamental infrastructure of modern Internet communication, making accurate router vendor identification a critical capability for multiple domains including network topology mapping, intelligent traffic engineering, and proactive cybersecurity defense. While Internet Protocol version 6 (IPv6) has achieved widespread global deployment as the next-generation Internet protocol, the opaque nature of its addressing mechanisms and protocol behaviors has created significant challenges in router attribute detection across IPv6 networks, leaving a crucial gap in network visibility and security analytics. To address this pressing challenge, we present IPv6 Router FingerPrinting (6RFP), an innovative lightweight fingerprinting methodology that establishes a new paradigm for IPv6 router vendor identification by systematically combining two complementary analytical dimensions: (i) comprehensive EUI-64 interface identifier analysis that captures vendor-specific hardware encoding patterns embedded in IPv6 addresses, and (ii) sophisticated IPv6 Identification Field characteristic profiling that reveals distinctive vendor implementations. Through extensive evaluation across diverse network environments, 6RFP demonstrates highly effective detection capabilities, achieving 85.79% accuracy—representing a remarkable 86.01% improvement over current state-of-the-art techniques—while maintaining minimal computational overhead suitable for real-time deployment. [ABSTRACT FROM AUTHOR] |
|
Copyright of Computer Journal is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Engineering Source |
