Affiliation:
1. University of California, Riverside, Riverside, CA, USA
Abstract
How can we build a definitive capability for tracking C2 servers? Having a large-scale continuously updating capability would be essential for understanding the spatiotemporal behaviors of C2 servers and, ultimately, for helping contain botnet activities. Unfortunately, existing information from threat intelligence feeds and previous works is often limited to a specific set of botnet families or short-term data collections. Responding to this need, we present C2Store, an initiative to provide the most comprehensive information on C2 servers. Our work makes the following contributions: (a) we develop techniques to collect, verify, and combine C2 server addresses from five types of sources, including uncommon platforms, such as GitHub and Twitter; (b) we create an open-access annotated database of 335,967 C2 servers across 133 malware families, which supports semantically-rich and smart queries; (c) we identify surprising behaviors of C2 servers with respect to their spatiotemporal patterns and behaviors. First, we successfully mine Twitter and GitHub and identify C2 servers with a precision of 97% and 94%, respectively. Furthermore, we find that the threat feeds identify only 24% of the servers in our database, with Twitter and GitHub providing 32%. A surprising observation is the identification of 250 IP addresses, each of which hosts more than 5 C2 servers for different botnet families at the same time. Overall, we envision C2Store as an ongoing effort that will facilitate research by providing timely, historical, and comprehensive C2 server information by critically combining multiple sources of information.
Publisher
Association for Computing Machinery (ACM)
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