West, Andrew G.Lee, Insup2023-05-222023-05-222011-09-012011-09-04https://repository.upenn.edu/handle/20.500.14332/6523IP blacklists are a well-regarded anti-spam mechanism that capture global spamming patterns. These properties make such lists a practical ground-truth by which to study email spam behaviors. Observing one blacklist for nearly a year-and-a-half, we collected data on roughly *half a billion* listing events. In this paper, that data serves two purposes. First, we conduct a measurement study on the dynamics of blacklists and email spam at-large. The magnitude/duration of the data enables scrutiny of long-term trends, at scale. Further, these statistics help parameterize our second task: the mining of blacklist history for temporal association rules. That is, we search for IP addresses with correlated histories. Strong correlations would suggest group members are not independent entities and likely share botnet membership. Unfortunately, we find that statistically significant groupings are rare. This result is reinforced when rules are evaluated in terms of their ability to: (1) identify shared botnet members, using ground-truth from botnet infiltrations and sinkholes, and (2) predict future blacklisting events. In both cases, performance improvements over a control classifier are nominal. This outcome forces us to re-examine the appropriateness of blacklist data for this task, and suggest refinements to our mining model that may allow it to better capture the dynamics by which botnets operate.© ACM 2011. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 8th Annual Collaboration, Electronic Messaging, Anti-Abuse, and Spam Conference, http://dx.doi.org/10.1145/2030376.2030385.CPS Internet of Thingsemail spamIP blacklistsmeasurement studytemporal data miningassociation rule learningnegative resultNumerical Analysis and Scientific ComputingOther Computer SciencesTheory and AlgorithmsPresentation