Spam-based advertising is a business. While it has engendered both widespread antipathy and a multi-billion dollar anti-spam industry, it continues to exist because it fuels a profitable enterprise. We lack, however, a solid understanding of this enterprise’s full structure, and thus most anti-spam interventions focus on only one facet of the overall spam value chain (e.g., spam filtering, URL blacklisting, site takedown). In this paper we present a holistic analysis that quantifies the full set of resources employed to monetize spam email— including naming, hosting, payment and fulfillment—using extensive measurements of three months of diverse spam data, broad crawling of naming and hosting infrastructures, and over 100 purchases from spam-advertised sites. We relate these resources to the organizations who administer them and then use this data to characterize the relative prospects for defensive interventions at each link in the spam value chain. In particular, we provide the first strong evidence of payment bottlenecks in the spam value chain; 95 % of spam-advertised pharmaceutical, replica and software products are monetized using merchant services from just a handful of banks.

Abstract. This paper studies the dynamics of scam hosting infrastructure, with an emphasis on the role of fast-flux service networks. By monitoring changes in DNS records of over 350 distinct spam-advertised domains collected from URLs in 115,000 spam emails received at a large spam sinkhole, we measure the rates and locations of remapping DNS records, and the rates at which “fresh ” IP addresses are used. We find that, unlike the short-lived nature of the scams themselves, the infrastructure that hosts these scams has relatively persistent features that may ultimately assist detection. 1

Recent Botnets such as Conficker, Kraken and Torpig have used DNS based“domain fluxing”for command-and-control, where each Bot queries for existence of a series of domain names and the owner has to register only one such domain name. In this paper, we develop a methodology to detect such“domain fluxes”in DNS traffic by looking for patterns inherent to domain names that are generated algorithmically, in contrast to those generated by humans. In particular, we look at distribution of alphanumeric characters as well as bigrams in all domains that are mapped to the same set of IP-addresses. We present and compare the performance of several distance metrics, including KL-distance, Edit distance and Jaccard measure. We train by using a good data set of domains obtained via a crawl of domains mapped to all IPv4 address space and modeling bad data sets based on behaviors seen so far and expected. We also apply our methodology to packet traces collected at a Tier-1 ISP and show we can automatically detect domain fluxing as used by Conficker botnet with minimal false positives.

Over the last few years, researchers and network operators have examined networks of DDoS agents, more recently called botnets due to their connection to Internet Relay Chat (IRC). In the continued quest to take down these networks of bots, two important questions arise: how many bots are there, and how to find every last bot? When one reads about a ten thousand, hundred thousand, one million node botnet, how much credibility does it have? Is botnet A really bigger than botnet B? The difficulty in accurately and stealthily assessing the size of the botnet often lies in the structure of the botnet itself, such as IRC, HTTP, P2P-based, or a hybrid thereof. We present a general overview of discovery techniques for networks of malware, and provide a glimpse at a two-year study of a P2P botnet. 1

The domain name service (DNS) plays an important role in the operation of the Internet, providing a two-way mapping between domain names and their numerical identifiers. Given its fundamental role, it is not surprising that a wide variety of malicious activities involve the domain name service in one way or another. For example, bots resolve DNS names to locate their command and control servers, and spam mails contain URLs that link to domains that resolve to scam servers. Thus, it seems beneficial to monitor the use of the DNS system for signs that indicate that a certain name is used as part of a malicious operation. In this paper, we introduce EXPOSURE, a system that employs large-scale, passive DNS analysis techniques to detect domains that are involved in malicious activity. We use 15 features that we extract from the DNS traffic that allow us to characterize different properties of DNS names and the ways that they are queried. Our experiments with a large, real-world data set consisting of 100 billion DNS requests, and a real-life deployment for two weeks in an ISP show that our approach is scalable and that we are able to automatically identify unknown malicious domains that are misused in a variety of malicious activity (such as for botnet command and control, spamming, and phishing). 1

Abstract. The Asprox infection weaves a complex chain of dependencies involving bots that perform SQL injections on vulnerable web servers, and visitors whose machines get compromised simply by visiting infected websites. Using real-world data sets, we study Asprox bots, infected web servers, and the malicious infrastructure behind Asprox propagation. We find that the malware-propagation infrastructure in Asprox is aggressively provisioned to resist take-down efforts. This, combined with the easy availability of vulnerable user machines and web servers whose administrators are probably constrained in time and resources necessary to fix the problem, indicates that cleaning up Asprox infections is not going to be easy. Keywords: Asprox, Malware, SQL Injection, Security. 1

Abstract—In this paper we propose a novel, passive approach for detecting and tracking malicious flux service networks. Our detection system is based on passive analysis of recursive DNS (RDNS) traffic traces collected from multiple large networks. Contrary to previous work, our approach is not limited to the analysis of suspicious domain names extracted from spam emails or precompiled domain blacklists. Instead, our approach is able to detect malicious flux service networks in-the-wild, i.e., as they are accessed by users who fall victims of malicious content advertised through blog spam, instant messaging spam, social website spam, etc., beside email spam. We experiment with the RDNS traffic passively collected at two large ISP networks. Overall, our sensors monitored more than 2.5 billion DNS queries per day from millions of distinct source IPs for a period of 45 days. Our experimental results show that the proposed approach is able to accurately detect malicious flux service networks. Furthermore, we show how our passive detection and tracking of malicious flux service networks may benefit spam filtering applications. I.

Abstract. Skype is one of the most used P2P applications on the Internet: VoIP calls, instant messaging, SMS and other features are provided at a low cost to millions of users. Although Skype is a closed source application, an API allows developers to build custom plugins which interact over the Skype network, taking advantage of its reliability and capability to easily bypass firewalls and NAT devices. Since the protocol is completely undocumented, Skype traffic is particularly hard to analyze and to reverse engineer. We propose a novel botnet model that exploits an overlay network such as Skype to build a parasitic overlay, making it extremely difficult to track the botmaster and disrupt the botnet without damaging legitimate Skype users. While Skype is particularly valid for this purpose due to its abundance of features and its widespread installed base, our model is generically applicable to distributed applications that employ overlay networks to send direct messages between nodes (e.g., peer-to-peer software with messaging capabilities). We are convinced that similar botnet models are likely to appear into the wild in the near future and that the threats they pose should not be underestimated. Our contribution strives to provide the tools to correctly evaluate and understand the possible evolution and deployment of this phenomenon. 1

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Recent Botnets Such As Conficker ... have used DNS based “domain fluxing” for command-and-control, where each Bot queries for existence of a series of domain names and the owner has to register only one such domain name. In this paper, we develop a methodology to detect such “domain fluxes” in DNS traffic by looking for patterns inherent to domain names that are generated algorithmically, in contrast to those generated by humans. In particular, we look at distribution of alphanumeric characters as well as bigrams in all domains that are mapped to the same set of IP-addresses. We present and compare the performance of several distance metrics, including KL-distance, Edit distance and Jaccard measure. We train by using a good data set of domains obtained via a crawl of domains mapped to all IPv4 address space and modeling bad data sets based on behaviors seen so far and expected. We also apply our methodology to packet traces collected at a Tier-1 ISP and show we can automatically detect domain fluxing as used by Conficker botnet with minimal false positives, in addition to discovering a new botnet within the ISP trace. We also analyze a campus DNS trace to detect another unknown botnet exhibiting advanced domain name generation technique.