No abstract

In a cyber meta-reality filled with zero-day exploits, autonomous code, Worms modeled from Stuxnet, and the coming onslaught of AI enabled malware, the need for actionable, virtually prescient threat intelligence is paramount. It is almost uniformly recognized that the current reactionary paradigm concerning cyber threats is severely lacking. Cyber threat analysts desperately need a systematic approach to cyber threat characterization and how cyber threats evolve within a greater construct defined here as the cyber microbiome. This paper will first define the concept of the cyber microbiome and its place in what has become the cyber meta-reality. Cyber threats will then be examined in relation to this paradigm and recommendations will be made regarding how threat characterization and genetic mutation can be examined in light of this new, techno-biological understanding.

DNS resolvers perform the essential role of translating domain names into IP addresses. The default DNS resolver offered by an Internet Service Provider (ISP) can be undesirable for a number of reasons such as censorship, lack of malware filtering options and low service quality. In this paper, we propose a novel method for estimating the amount of DNS traffic directed at non-ISP resolvers by using DNS and NetFlow data from an ISP. This method is extended to also estimate the amount of DNS traffic towards resolvers that offer malware filtering or parental control functionality. Finally, we propose a novel method for estimating the amount of DNS traffic at non-ISP resolvers that would have been censored by ISP resolvers. The results of applying these methods on an ISP dataset shows to which extent 3rd party resolvers are chosen by users for either malware filtering or censorship circumvention purposes.

Botnets frequently use DGA and fast-flux techniques to ensure the availability of their command and control (CnC) infrastructure. However, the CnC IP addresses are still exposed in plain-text in publicly available DNS A records, which can be exploited by defenders to disrupt botnet operations. This paper presents the concept of the IP Generation Algorithm (IGA) as a novel method, usable by botmasters, to encrypt the CnC IP address in DNS records to avoid plain-text IP address exposure. This raises the bar for blacklisting malicious IP addresses, and can also be combined with existing techniques to further harden the CnC. For use by defenders, an IGA botnet detection method based on the combination of DNS and NetFlow data is presented and validated using an emulated botnet and an ISP data set.

DNS hijacking represents a security threat to users because it enables bypassing existing DNS security measures. Several malware families exploit this by changing the client DNS configuration to point to a malicious DNS resolver. Following the assumption that users will never actively choose to use a resolver that is not well-known, our paper introduces the idea of detecting client-based DNS hijacking by classifying public resolvers based on whether they are well-known or not. Furthermore, we propose to use NetFlow-based features to classify a resolver as well-known or malicious. By characterizing and manually labelling the 405 resolvers seen in four weeks of NetFlow data from a national ISP, we show that classification of both well-known and malicious servers can be made with an AUROC of 0.85.