Abstract. Modern botnets rely on domain-generation algorithms (DGAs) to build resilient command-and-control infrastructures. Given the prevalence of this mechanism, recent work has focused on the analysis of DNS traffic to recognize botnets based on their DGAs. While previous work has concentrated on detection, we focus on supporting intelligence operations. We propose Phoenix, a mechanism that, in addition to telling DGA-and non-DGA-generated domains apart using a combination of string and IP-based features, characterizes the DGAs behind them, and, most importantly, finds groups of DGA-generated domains that are representative of the respective botnets. As a result, Phoenix can associate previously unknown DGA-generated domains to these groups, and produce novel knowledge about the evolving behavior of each tracked botnet. We evaluated Phoenix on 1,153,516 domains, including DGA-generated domains from modern, well-known botnets: without supervision, it correctly distinguished DGA-vs. non-DGA-generated domains in 94.8 percent of the cases, characterized families of domains that belonged to distinct DGAs, and helped researchers "on the field" in gathering intelligence on suspicious domains to identify the correct botnet.
In the case of REF , the authors are able to characterize and classify similar DGA-generated domains, generating knowledge about the evolving behaviour of botnets.
Phoenix: DGA-Based Botnet Tracking and Intelligence
DIMVA
Computer Science