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This paper focuses on reporting of Internet malicious activity (ormal-activity in short) by public blacklists with the objective of providing asystematic characterization of what has been reported over the years, and moreimportantly, the evolution of reported activities. Using an initial seed of 22blacklists, covering the period from January 2007 to June 2017, we collect morethan 51 million mal-activity reports involving 662K unique IP addressesworldwide. Leveraging the Wayback Machine, antivirus (AV) tool reports andseveral additional public datasets (e.g., BGP Route Views and Internetregistries) we enrich the data with historical meta-information includinggeo-locations (countries), autonomous system (AS) numbers and types ofmal-activity. Furthermore, we use the initially labelled dataset of approx 1.57million mal-activities (obtained from public blacklists) to train a machinelearning classifier to classify the remaining unlabeled dataset of approx 44million mal-activities obtained through additional sources. We make our uniquecollected dataset (and scripts used) publicly available for further research.  The main contributions of the paper are a novel means of report collection,with a machine learning approach to classify reported activities,characterization of the dataset and, most importantly, temporal analysis ofmal-activity reporting behavior. Inspired by P2P behavior modeling, ouranalysis shows that some classes of mal-activities (e.g., phishing) and a smallnumber of mal-activity sources are persistent, suggesting that eitherblacklist-based prevention systems are ineffective or have unreasonably longupdate periods. Our analysis also indicates that resources can be betterutilized by focusing on heavy mal-activity contributors, which constitute thebulk of mal-activities.

A Decade of Mal-Activity Reporting: A Retrospective Analysis of Internet Malicious Activity Blacklists