Modern botnets rely on domain-generation algorithms (DGAs) to build resilient command-and-control infrastructures. Recent works focus on recognizing automatically generated domains (AGDs) from DNS traffic, which potentially allows to identify previously unknown AGDs to hinder or disrupt botnets' communication capabilities. The state-of-the-art approaches require to deploy low-level DNS sensors to access data whose collection poses practical and privacy issues, making their adoption problematic. We propose a mechanism that overcomes the above limitations by analyzing DNS traffic data through a combination of linguistic and IP-based features of suspicious domains. In this way, we are able to identify AGD names, characterize their DGAs and isolate logical groups of domains that represent the respective botnets. Moreover, our system enriches these groups with new, previously unknown AGD names, and produce novel knowledge about the evolving behavior of each tracked botnet. We used our system in real-world settings, to help researchers that requested intelligence on suspicious domains and were able to label them as belonging to the correct botnet automatically. Additionally, we ran an evaluation on 1,153,516 domains, including AGDs from both modern (e.g., Bamital) and traditional (e.g., Conficker, Torpig) botnets. Our approach correctly isolated families of AGDs that belonged to distinct DGAs, and set automatically generated from non-automatically generated domains apart in 94.8 percent of the cases.
The ability to remote-control infected PCs is a fundamental component of modern malware campaigns. At the same time, the command and control (C&C) infrastructure that provides this capability is an attractive target for mitigation. In recent years, more or less successful takedown operations have been conducted against botnets employing both client-server and peer-to-peer C&C architectures. To improve their robustness against such disruptions of their illegal business, botnet operators routinely deploy redundant C&C infrastructure and implement failover C&C strategies. In this paper, we propose techniques based on multi-path exploration [1] to discover how malware behaves when faced with the simulated take-down of some of the network endpoints it communicates with. We implement these techniques in a tool called Squeeze, and show that it allows us to detect backup C&C servers, increasing the coverage of an automatically generated C&C blacklist by 19.7 , and can trigger domain generation algorithms that malware implements for disaster-recovery.