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.
focused on domains that are malicious, in general, from the viewpoint of the victims of attacks perpetrated through botnets (e.g., phishing, spam, drive-by download). Instead, focuses on AGDs and, for this reason, it models the features of the DNS layer between bots and C &C servers. Moreover, the detection method of @cite_1 is based on supervised learning, whereas uses unsupervised techniques.
@cite_1
1981049515
In this paper, we present FluxBuster, a novel passive DNS traffic analysis system for detecting and tracking malicious flux networks. FluxBuster applies large-scale monitoring of DNS traffic traces generated by recursive DNS (RDNS) servers located in hundreds of different networks scattered across several different geographical locations. Unlike most previous work, our detection approach is not limited to the analysis of suspicious domain names extracted from spam emails or precompiled domain blacklists. Instead, FluxBuster is able to detect malicious flux service networks in-the-wild, i.e., as they are "accessed” by users who fall victim of malicious content, independently of how this malicious content was advertised. We performed a long-term evaluation of our system spanning a period of about five months. The experimental results show that FluxBuster is able to accurately detect malicious flux networks with a low false positive rate. Furthermore, we show that in many cases FluxBuster is able to detect malicious flux domains several days or even weeks before they appear in public domain blacklists.