Botnets allow adversaries to wage attacks on unprecedented scales at unprecedented rates, motivation for which is no longer just malice but profits instead. The longer botnets go undetected, the higher those profits.

I present in this thesis an architecture that leverages collaborative networks of peers in order to detect bots across the same. Not only is this architecture both automated and rapid, it is also high in true positives and low in false positives. More over, it accepts as realities insecurities in today's systems, tolerating bugs, complexity, monocultures, and interconnectivity alike. This architecture embodies my own definition of anomalous behavior: I say a system's behavior is anomalous if it correlates all too well with other networked, but otherwise independent, systems' behavior.

I provide empirical validation that collaborative detection of bots can indeed work. I validate my ideas in both simulation and the wild. Through simulations with traces of 9 variants of worms and 25 non-worms, I find that two peers, upon exchanging summaries of system calls recently executed, can decide that they are, more likely than not, both executing the same worm as often as 97% of the time. I deploy an actual prototype of my architecture to a network of 29 systems with which I monitor and analyze 10,776 processes, inclusive of 511 unique non-worms (873 if unique versions constitute unique non-worms). Using that data, I expose the utility of temporal consistency (similarity over time in worms' and non-worms' invocations of system calls) in collaborative detection.

I identify properties with which to distinguish non-worms from worms 99% of the time. I find that a collaborative network, using patterns of system calls and simple heuristics, can detect worms running on multiple hosts. And I find that collaboration among peers significantly reduces the risk of false positives because of the unlikely, simultaneous appearance across peers of non-worm processes with, worm-like properties.