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.
proposed a system that detects C &C failover strategies with techniques based on multi-path exploration. The system explores the behavior of malware samples during simulated network failures. Backup C &C servers and AGDs are so unveiled, leading to new blacklists. The approach is very promising toward expanding blacklists of malicious domains, although it may produce misleading results when the malware behavior depends on time-dependent information. Differently from @cite_3 , discovers new AGDs---and other knowledge---using solely passive, recursive-level DNS traffic and requires no malware samples to work.
@cite_3
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The ability to remote-control infected PCs is a fundamental component of modern malware campaigns. At the same time, the command and control (C&C) infrastructure that provides this capability is an attractive target for mitigation. In recent years, more or less successful takedown operations have been conducted against botnets employing both client-server and peer-to-peer C&C architectures. To improve their robustness against such disruptions of their illegal business, botnet operators routinely deploy redundant C&C infrastructure and implement failover C&C strategies. In this paper, we propose techniques based on multi-path exploration [1] to discover how malware behaves when faced with the simulated take-down of some of the network endpoints it communicates with. We implement these techniques in a tool called Squeeze, and show that it allows us to detect backup C&C servers, increasing the coverage of an automatically generated C&C blacklist by 19.7 , and can trigger domain generation algorithms that malware implements for disaster-recovery. Many botnet detection systems employ a blacklist of known command and control (C&C) domains to detect bots and block their traffic. Similar to signature-based virus detection, such a botnet detection approach is static because the blacklist is updated only after running an external (and often manual) process of domain discovery. As a response, botmasters have begun employing domain generation algorithms (DGAs) to dynamically produce a large number of random domain names and select a small subset for actual C&C use. That is, a C&C domain is randomly generated and used for a very short period of time, thus rendering detection approaches that rely on static domain lists ineffective. Naturally, if we know how a domain generation algorithm works, we can generate the domains ahead of time and still identify and block bot-net C&C traffic. The existing solutions are largely based on reverse engineering of the bot malware executables, which is not always feasible. In this paper we present a new technique to detect randomly generated domains without reversing. Our insight is that most of the DGA-generated (random) domains that a bot queries would result in Non-Existent Domain (NXDomain) responses, and that bots from the same bot-net (with the same DGA algorithm) would generate similar NXDomain traffic. Our approach uses a combination of clustering and classification algorithms. The clustering algorithm clusters domains based on the similarity in the make-ups of domain names as well as the groups of machines that queried these domains. The classification algorithm is used to assign the generated clusters to models of known DGAs. If a cluster cannot be assigned to a known model, then a new model is produced, indicating a new DGA variant or family. We implemented a prototype system and evaluated it on real-world DNS traffic obtained from large ISPs in North America. We report the discovery of twelve DGAs. Half of them are variants of known (botnet) DGAs, and the other half are brand new DGAs that have never been reported before. Various families of malware use domain generation algorithms (DGAs) to generate a large number of pseudo-random domain names to connect to a command and control (C&C) server. In order to block DGA C&C traffic, security organizations must first discover the algorithm by reverse engineering malware samples, then generating a list of domains for a given seed. The domains are then either preregistered or published in a DNS blacklist. This process is not only tedious, but can be readily circumvented by malware authors using a large number of seeds in algorithms with multivariate recurrence properties (e.g., banjori) or by using a dynamic list of seeds (e.g., bedep). Another technique to stop malware from using DGAs is to intercept DNS queries on a network and predict whether domains are DGA generated. Such a technique will alert network administrators to the presence of malware on their networks. In addition, if the predictor can also accurately predict the family of DGAs, then network administrators can also be alerted to the type of malware that is on their networks. This paper presents a DGA classifier that leverages long short-term memory (LSTM) networks to predict DGAs and their respective families without the need for a priori feature extraction. Results are significantly better than state-of-the-art techniques, providing 0.9993 area under the receiver operating characteristic curve for binary classification and a micro-averaged F1 score of 0.9906. In other terms, the LSTM technique can provide a 90 detection rate with a 1:10000 false positive (FP) rate---a twenty times FP improvement over comparable methods. Experiments in this paper are run on open datasets and code snippets are provided to reproduce the results. A novel approach to malware detection by recognizing known inter-process and intra-process malicious functionalities in software behavior is proposed. It encompasses two essential tasks: the specification of a functionality that may involve a joint activity of several apparently independent processes, and efficient recognition of the specified functionality in the process behavior. The robustness of the proposed technology is achieved by the generalization of the specification domain that is separated from the detection domain. The functionalities of interest are defined in the abstract system domain through activity diagrams, thus resulting in formal specifications that are rather generic and less prone to false negatives. To facilitate the detection, we developed a procedure that automatically generates a Colored Petri Net recognizing the specified functionality in the system call domain. The separation of specification and recognition domains results in signature expressiveness and recognition efficiency. The approach is illustrated by the analysis, specification and consequent recognition of several common malicious functionalities including self-replication engines and popular payloads. A prototype IDS implementing the proposed approach has been developed and successfully tested on a set of real malware.