The Border Gateway Protocol (BGP) is one of the fundamental computer communication protocols. Monitoring and mining BGP update messages can directly reveal the health and stability of Internet routing. Here we make two contributions: firstly we find patterns in BGP updates, like self-similarity, power-law and lognormal marginals; secondly using these patterns, we find anomalies. Specifically, we develop BGP-lens, an automated BGP updates analysis tool, that has three desirable properties: (a) It is effective, able to identify phenomena that would otherwise go unnoticed, such as a peculiar `clothesline' behavior or prolonged `spikes' that last as long as 8 hours; (b) It is scalable, using algorithms are all linear on the number of time-ticks; and (c) It is admin-friendly, giving useful leads for phenomenon of interest.

We showcase the capabilities of BGP-lens by identifying surprising phenomena verified by syadmins, over a massive trace of BGP updates spanning 2 years, from the publicly available site datapository.net.