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In the last decade, the use of fast flux technique has become established asa common practice to organise botnets in Fast Flux Service Networks (FFSNs),which are platforms able to sustain illegal online services with very highavailability. In this paper, we report on an effective fast flux detectionalgorithm based on the passive analysis of the Domain Name System (DNS) trafficof a corporate network. The proposed method is based on the near-real-timeidentification of different metrics that measure a wide range of fast flux keyfeatures; the metrics are combined via a simple but effective mathematical anddata mining approach. The proposed solution has been evaluated in a one-monthexperiment over an enterprise network, with the injection of pcaps associatedwith different malware campaigns, that leverage FFSNs and cover a wide varietyof attack scenarios. An in-depth analysis of a list of fast flux domainsconfirmed the reliability of the metrics used in the proposed algorithm andallowed for the identification of many IPs that turned out to be part of twonotorious FFSNs, namely Dark Cloud and SandiFlux, to the description of whichwe therefore contribute. All the fast flux domains were detected with a verylow false positive rate; a comparison of performance indicators with previousworks show a remarkable improvement.

Fast Flux Service Network Detection via Data Mining on Passive DNS Traffic