In the last decade, the use of fast flux technique has become established as a common practice to organise botnets in Fast Flux Service Networks (FFSNs), which are platforms able to sustain illegal online services with very high availability. In this paper, we report on an effective fast flux detection algorithm based on the passive analysis of the Domain Name System (DNS) traffic of a corporate network. The proposed method is based on the near-real-time identification of different metrics that measure a wide range of fast flux key features; the metrics are combined via a simple but effective mathematical and data mining approach. The proposed solution has been evaluated in a one-month experiment over an enterprise network, with the injection of pcaps associated with different malware campaigns, that leverage FFSNs and cover a wide variety of attack scenarios. An in-depth analysis of a list of fast flux domains confirmed the reliability of the metrics used in the proposed algorithm and allowed for the identification of many IPs that turned out to be part of two notorious FFSNs, namely Dark Cloud and SandiFlux, to the description of which we therefore contribute. All the fast flux domains were detected with a very low false positive rate; a comparison of performance indicators with previous works show a remarkable improvement.
A large number of approaches have been proposed to detect FFSNs and to distinguish them from legitimate CDNs and RRDNSs. Most of them rely on active DNS analysis, which allows for the collection of a large number of IPs associated with a domain, thus simplifying the FFSNs detection, but they require the resolutions of domains that may be associated with malicious activities @cite_29 @cite_30 @cite_5 @cite_0 @cite_18 . These methods, despite being appropriate for a deep analysis of FFSNs, have relevant drawbacks in implementations oriented to the monitoring of corporate networks @cite_7 @cite_34 .
@cite_30 @cite_18 @cite_7 @cite_29 @cite_0 @cite_5 @cite_34
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A new DNS technique called Fast-Flux Service Network (FFSN) has been employed by bot herders to hide malicious activities and extend the lifetime of malicious root servers. Although various methods have been proposed for detecting FFSNs, these mechanisms have low detection accuracy and protracted detection time. This study presents a novel detection scheme, designated as the Genetic-based ReAl-time DEtection (GRADE) system, to identify FFSNs in real time. GRADE differentiates between FFSNs and benign services by employing two new characteristics: the entropy of domains of preceding nodes for all A records and the standard deviation of round trip time to all A records. By applying genetic algorithms, GRADE is able to find the best strategy to detect current FFSN trends. Empirical results show GRADE has very high detection accuracy ( 98 ) and gives results within a few seconds. It provides considerable improvement over existing reference schemes such Flux-Score [8], SSFD [13], and FFSD [14]. Fast-flux service networks (FFSN) helps the cyber-criminals to hide the servers used for malicious activities behind a wall of proxies (bots). It provides the reliability and detection evasion to a malicious server. FFSN use a large pool of IP addresses for proxies. Detection of FFSN is difficult as few benign technologies like Content distribution networks and round robin DNS have similar working characteristics. Many approaches have been proposed to detect FFSN and fast flux domains. However, due to dynamic behavior of FFSN, these techniques suffer from a significant number of false positives. In this paper, we present a Temporal and Real time detections based approach (TempR) to detect fast flux domains. The features of fast flux domains and benign domains have been collected and classified using intelligent classifiers. Our technique illustrates 96.99 detection accuracy with the recent behavior of fast flux domains. We present the first empirical study of fast-flux service networks (FFSNs), a newly emerging and still not widelyknown phenomenon in the Internet. FFSNs employ DNS to establish a proxy network on compromised machines through which illegal online services can be hosted with very high availability. Through our measurements we show that the threat which FFSNs pose is significant: FFSNs occur on a worldwide scale and already host a substantial percentage of online scams. Based on analysis of the principles of FFSNs, we develop a metric with which FFSNs can be effectively detected. Considering our detection technique we also discuss possible mitigation strategies. DNS is widely abused by Internet criminals in order to provide reliable communication within malicious network infrastructure as well as flexible and resilient hosting of malicious content. This paper presents a novel detection method that can be used for identifying potentially compromised clients based on DNS traffic analysis. The proposed method identifies suspicious agile DNS mappings, i.e., mappings characterized by fast changing domain names or and IP addresses, often used by malicious services. The approach discovers clients that have queried domains contained within identified suspicious domain-to-IP mappings, thus assisting in pinpointing potentially compromised clients within the network. The proposed approach targets compromised clients in large-scale operational networks. We have evaluated the proposed approach using an extensive set of DNS traffic traces from different operational ISP networks. The evaluation illustrates a great potential of accurately identifying suspicious domain-to-IP mappings and potentially compromised clients. Furthermore, the achieved performance indicate that the novel detection approach is promising in view of the adoption in operational ISP networks. Finally, the proposed approach targets both Fast-flux and Domain-flux, thus having an advantage over existing detection methods that identify compromised clients.