Current large-scale topology mapping systems require multiple days to
characterize the Internet due to the large amount of probing traffic they
incur. The accuracy of maps from existing systems is unknown, yet empirical
evidence suggests that additional fine-grained probing exposes hidden links
and temporal dynamics. Through longitudinal analysis of data from the
Archipelago and iPlane systems, in conjunction with our own active probing,
we examine how to shorten Internet topology mapping cycle time. In
particular, this work develops discriminatory primitives that maximize
topological fidelity while being efficient.
Yarrp'ing the Internet,
CAIDA AIMS, San Diego, CA, 2016.
Recent Results in Network Mapping: Implications on Cybersecurity,
DHS S&T Cyber Seminar, Washington, DC, 2015.
Internet Mapping Primitives,
DHS Cyber Security Division PI Meeting, Washington, DC, 2014.
High-Frequency Active Internet Topology Mapping,
DHS Cyber Security Division PI Meeting, Berkeley, CA, 2014.
Deploying Efficient Internet Topology Primitives,
DHS Cyber Security Division PI Meeting, Washington, DC, 2013.
Toward High-Frequency Topology Mapping,
ACM IMC 2010.
- Yarrp: Yelling at Random Routers Progressively
- Arkqueue: Topology on Demand Wrapper for CAIDA's Ark
- scamper util: Native python implementation of scamper utils
- degreaser: Scanner to find network tarpits