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IPv6-Darknet Network Traffic Detection

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Artificial Intelligence and Security (ICAIS 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12737))

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Abstract

The state of the network can be reflected by the background traffic. Negative network measurements can be a very important way to understand the Internet. I would like to express appreciation to CERNET, who provided us with an IPv6 address space allocated but not a fully used network. By announcing a large /20 covering prefixes on this address, we have published routing information on China's domestic education network, business network, and foreign education network. Based on the honeypot method, we collect relative traffic at the last hop router of the experiment network. Thus, we make our experiment environment a network telescope. We discover that background radiation traffic grew more rapidly than it was years ago under the current ipv6 network situation. Moreover, suspicious IPv6 address scanning traffic shows up. We classify and analyze the traffic and classify all the source addresses and destination addresses. We found that the source addresses are mainly from Asian countries. In particular, we conduct further detection and monitor on the suspicious source addresses. We analyze the time when it appears and what it scans, including the destination address and the port type. The most interesting destination ports to the outside world are mainly 80, 8080, 443, 53, 21, 22, 23, and 25, which are related to web services and host system applications. We explain most of the data and highlight the significant attributes of the data. We found several special addresses scanning our address segment periodically. Our work reveals the situation and the problem under the current IPv6 network situation.

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Liu, C., Liu, Q., Hao, S., Bao, C., Li, X. (2021). IPv6-Darknet Network Traffic Detection. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2021. Lecture Notes in Computer Science(), vol 12737. Springer, Cham. https://doi.org/10.1007/978-3-030-78612-0_19

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  • DOI: https://doi.org/10.1007/978-3-030-78612-0_19

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  • Print ISBN: 978-3-030-78611-3

  • Online ISBN: 978-3-030-78612-0

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