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: Randomized High-Speed Active Topology Discovery ACM Internet Measurement Conference (IMC), 2016.
• Uncovering Network Tarpits with Degreaser Annual Computer Security Applications Conference (ACSAC), 2014.
• Ingress Point Spreading: A New Primitive for Adaptive Active Network Mapping, Passive and Active Measurement (PAM) Conference, 2014.
• A Technique for Network Topology Deception Military Communications Conference (MILCOM), 2013.
• Primitives for Active Internet Topology Mapping: Toward High-Frequency Characterization , ACM Internet Measurement Conference (IMC), 2010.

• 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

• DHS S&T CyberSecurity BAA 11-02