Enabling network security through active DNS datasets

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Abstract

Most modern cyber crime leverages the Domain Name System (DNS) to attain high levels of network agility and make detection of Internet abuse challenging. The majority of malware, which represent a key component of illicit Internet operations, are programmed to locate the IP address of their command-and-control (C&C) server through DNS lookups. To make the malicious infrastructure both agile and resilient, malware authors often use sophisticated communication methods that utilize DNS (i.e., domain generation algorithms) for their campaigns. In general, Internet miscreants make extensive use of short-lived disposable domains to promote a large variety of threats and support their criminal network operations. To effectively combat Internet abuse, the security community needs access to freely available and open datasets. Such datasets will enable the development of new algorithms that can enable the early detection, tracking, and overall lifetime of modern Internet threats. To that end, we have created a system, Thales, that actively queries and collects records for massive amounts of domain names from various seeds. These seeds are collected from multiple public sources and, therefore, free of privacy concerns. The results of this effort will be opened and made freely available to the research community. With three case studies we demonstrate the detection merit that the collected active DNS datasets contain. We show that (i) more than 75% of the domain names in public black lists (PBLs) appear in our datasets several weeks (and some cases months) in advance, (ii) existing DNS research can be implemented using only active DNS, and (iii) malicious campaigns can be identified with the signal provided by active DNS.

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APA

Kountouras, A., Kintis, P., Lever, C., Chen, Y., Nadji, Y., Dagon, D., … Joffe, R. (2016). Enabling network security through active DNS datasets. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9854 LNCS, pp. 188–208). Springer Verlag. https://doi.org/10.1007/978-3-319-45719-2_9

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