October 2018 – ANT Research News

We have published a new paper “Who Knocks at the IPv6 Door? Detecting IPv6 Scanning” by Kensuke Fukuda and John Heidemann, in the ACM Internet Measurements Conference (IMC 2018) in Boston, Mass., USA.

From the abstract:

DNS backscatter detects internet-wide activity by looking for common reverse DNS lookups at authoritative DNS servers that are high in the DNS hierarchy. Both DNS backscatter and monitoring unused address space (darknets or network telescopes) can detect scanning in IPv4, but with IPv6’s vastly larger address space, darknets become much less effective. This paper shows how to adapt DNS backscatter to IPv6. IPv6 requires new classification rules, but these reveal large network services, from cloud providers and CDNs to specific services such as NTP and mail. DNS backscatter also identifies router interfaces suggesting traceroute-based topology studies. We identify 16 scanners per week from DNS backscatter using observations from the B-root DNS server, with confirmation from backbone traffic observations or blacklists. After eliminating benign services, we classify another 95 originators in DNS backscatter as potential abuse. Our work also confirms that IPv6 appears to be less carefully monitored than IPv4.

We released a new technical report “Plumb: Efficient Processing of Multi-Users Pipelines (Extended)”, by Abdul Qadeer and John Heidemann, as ISI-TR-727. It is available at https://www.isi.edu/publications/trpublic/pdfs/isi-tr-727.pdf

From the abstract:

Services such as DNS and websites often produce streams of data that are consumed by analytics pipelines operated by multiple teams. Often this data is processed in large chunks (megabytes) to allow analysis of a block of time or to amortize costs. Such pipelines pose two problems: first, duplication of computation and storage may occur when parts of the pipeline are operated by different groups. Second, processing can be lumpy, with structural lumpiness occurring when different stages need different amounts of resources, and data lumpiness occurring when a block of input requires increased resources. Duplication and structural lumpiness both can result in inefficient processing. Data lumpiness can cause pipeline failure or deadlock, for example if differences in DDoS traffic compared to normal can require 6× CPU. We propose Plumb, a framework to abstract file processing for a multi-stage pipeline. Plumb integrates pipelines contributed by multiple users, detecting and eliminating duplication of computation and intermediate storage. It tracks and adjusts computation of each stage, accommodating both structural and data lumpiness. We exercise Plumb with the processing pipeline for B-Root DNS traffic, where it will replace a hand-tuned system to provide one third the original latency by utilizing 22% fewer CPU and will address limitations that occur as multiple users process data and when DDoS traffic causes huge shifts in performance.