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The goal of this work is to systematically extract information from hackerforums, whose information would be in general described as unstructured: thetext of a post is not necessarily following any writing rules. By contrast,many security initiatives and commercial entities are harnessing the readilypublic information, but they seem to focus on structured sources ofinformation. Here, we focus on the problem of identifying malicious IPaddresses, among the IP addresses which are reported in the forums. We developa method to automate the identification of malicious IP addresses with thedesign goal of being independent of external sources. A key novelty is that weuse a matrix decomposition method to extract latent features of the behavioralinformation of the users, which we combine with textual information from therelated posts. A key design feature of our technique is that it can be readilyapplied to different language forums, since it does not require a sophisticatedNatural Language Processing approach. In particular, our solution only needs asmall number of keywords in the new language plus the users behavior capturedby specific features. We also develop a tool to automate the data collectionfrom security forums. Using our tool, we collect approximately 600K posts from3 different forums. Our method exhibits high classification accuracy, while theprecision of identifying malicious IP in post is greater than 88% in all threeforums. We argue that our method can provide significantly more information: wefind up to 3 times more potentially malicious IP address compared to thereference blacklist VirusTotal. As the cyber-wars are becoming more intense,having early accesses to useful information becomes more imperative to removethe hackers first-move advantage, and our work is a solid step towards thisdirection.

Mining actionable information from security forums: the case of malicious IP addresses