Advertising fraud, particularly click fraud, is a growing concern for the online advertising industry. The use of click bots, malware that automatically clicks on ads to generate fraudulent traffic, has steadily increased over the last years. While the security industry has focused on detecting and removing malicious binaries associated with click bots, a better understanding of how fraudsters operate within the ad ecosystem is needed to be able to disrupt it efficiently. This paper provides a detailed dissection of the advertising fraud scheme employed by Boaxxe, a malware specializing in click fraud. By monitoring its activities during a 7-month longitudinal study, we were able to create of map of the actors involved in the ecosystem enabling this fraudulent activity. We then applied a Social Network Analysis (SNA) technique to identify the key actors of this ecosystem that could be effectively influenced in order to maximize disruption of click-fraud monetization. The results show that it would be possible to efficiently disrupt the ability of click-fraud traffic to enter the legitimate market by pressuring a limited number of these actors. We assert that this approach would produce better long term effects than the use of take downs as it renders the ecosystem unusable for monetization.

Analyzing cryptographic implementations has important applications, especially for malware analysis where they are an integral part both of the malware payload and the unpacking code that decrypts this payload. These implementations are often based on well-known cryptographic functions, whose description is publicly available. While potentially very useful for malware analysis, the identification of such cryptographic primitives is made difficult by the fact that they are usually obfuscated. Current state-of-the-art identification tools are ineffective due to the absence of easily identifiable static features in obfuscated code. However, these implementations still maintain the input-output (I/O) relationship of the original function. In this paper, we present a tool that leverages this fact to identify cryptographic functions in obfuscated programs, by retrieving their I/O parameters in an implementation-independent fashion, and comparing them with those of known cryptographic functions. In experimental evaluation, we successfully identified the cryptographic functions TEA, RC4, AES and MD5 both in synthetic examples protected by a commercial-grade packer (AsProtect), and in several obfuscated malware samples (Sality, Waledac, Storm Worm and SilentBanker). In addition, our tool was able to recognize basic operations done in asymmetric ciphers such as RSA.

Botnets constitute a serious security problem. A lot of effort has been invested towards understanding them better, while developing and learning how to deploy effective counter-measures against them. Their study via various analysis, modelling and experimental methods are integral parts of the development cycle of any such botnet mitigation schemes. It also constitutes a vital part of the process of understanding present threats and predicting future ones. Currently, the most popular of these techniques are botnet studies, where researchers interact directly with real-world botnets. This approach is less than ideal, for many reasons that we discuss this paper, including scientific validity, ethical and legal issues. Consequently, we present an alternative approach employing in the lab experiments involving at-scale emulated botnets. We discuss the advantages of such an approach over reverse engineering, analytical modelling, simulation and in-the-wild studies. Moreover, we discuss the requirements that facilities supporting them must have. We then describe an experiment which we emulated a 3000-node, fully-featured version of the Waledac botnet, complete with an emulated command and control (C&C) infrastructure. By observing the load characteristics and yield (rate of spamming) of such a botnet, we can draw interesting conclusions about its real-world operations and design decisions made by its creators. Furthermore, we conducted experiments with sybil attacks launched against it and verified their viability. However, we were able to determine that mounting such attacks is not so simple: high resource consumption can cause havoc and partially neutralise them. Finally, we were able to repeat the attacks with varying parameters, an attempt to optimise them. The merits of this experimental approach is underlined since by the fact that it would have been difficult to obtain these results by other methods.