Subnet discovery in passive internally sensed network tomography
Passive internally-sensed network tomography is the study of network characteristics and behavior based on observations of network traffic on a select subset of links inside the network. In this research, we identify the Subnet Discovery Problem as a key challenge in performing passive network tomography, and propose an alternating classification tree based algorithm for addressing it. This Subnet Discovery Algorithm clusters network end-hosts into CIDR style subnets without requiring prior information, and has applications for predicting network routes and for the detection of IP address spoofing. As a pre-processing technique, it has the potential to improve the performance of a variety of network tomography algorithms. We evaluate the performance of the algorithm in simulations and on real data gathered from the Abilene network.
King, Ryan C.. "Subnet discovery in passive internally sensed network tomography." (2007) Master’s Thesis, Rice University. https://hdl.handle.net/1911/20514.