learning network structure from passive measurements
The ability to discover network organization, whether in the form of explicit topology reconstruction or as embeddings that approximate topological distance, is a valuable tool. To date, network discovery has been based on active measurements. However, it is feasible to envision passive discovery of network topology and distance, simply by monitoring packet traffic. Unfortunately, the lack of explicit control over the choices of which endpoints are measured means that passive network discovery must deal with the problem of missing information. We consider one such example, namely reconstructing embeddings and some network structure information from unwanted network traffic captured at a set of honeypots. We develop a number of algorithms for reconstruction of missing measurements. Our algorithms use insights derived from the known topology of the Internet as well as local imputation techniques from approximation theory. We characterize the degree to which missing information can be reconstructed and show that a limited but useful amount of reconstruction is possible, allowing the recovery of network embeddings and some topological relationships from passively collected data.
learning network structure measurement organization topology reconstruction embedding approximation distance tool discoveries active measurement network topologies monitor traffic control informal examples algorithm use internet locality imputation theory recovery relationships data inference