Spamalytics: an empirical analysis of spam marketing conversion

• Authors: Chris Kanich;Christian Kreibich;Kirill Levchenko;Brandon Enright;Geoffrey M. Voelker;Vern Paxson;Stefan Savage
• Venue: Communications of the ACM - The Status of the P versus NP Problem
• Year: 2009

Spam-based marketing is a curious beast. We all receive the advertisements---"Excellent hardness is easy!"---but few of us have encountered a person who admits to following through on this offer and making a purchase. And yet, the relentlessness by which such spam continually clogs Internet inboxes, despite years of energetic deployment of antispam technology, provides undeniable testament that spammers find their campaigns profitable. Someone is clearly buying. But how many, how often, and how much? Unraveling such questions is essential for understanding the economic support for spam and hence where any structural weaknesses may lie. Unfortunately, spammers do not file quarterly financial reports, and the underground nature of their activities makes third-party data gathering a challenge at best. Absent an empirical foundation, defenders are often left to speculate as to how successful spam campaigns are and to what degree they are profitable. For example, IBM's Joshua Corman was widely quoted as claiming that spam sent by the Storm worm alone was generating "millions and millions of dollars every day."1 While this claim could in fact be true, we are unaware of any public data or methodology capable of confirming or refuting it. The key problem is our limited visibility into the three basic parameters of the spam value proposition: the cost to send spam, offset by the "conversion rate" (probability that an email sent will ultimately yield a "sale"), and the marginal profit per sale. The first and last of these are self-contained and can at least be estimated based on the costs charged by third-party spam senders and through the pricing and gross margins offered by various Interne marketing "affiliate programs."a However, the conversion rate depends fundamentally on group actions---on what hundreds of millions of Internet users do when confronted with a new piece of spam---and is much harder to obtain. While a range of anecdotal numbers exist, we are unaware of any well-documented measurement of the spam conversion rate.b In part, this problem is methodological. There are no apparent methods for indirectly measuring spam conversion. Thus, the only obvious way to extract this data is to build an e-commerce site, market it via spam, and then record the number of sales. Moreover, to capture the spammer's experience with full fidelity, such a study must also mimic their use of illicit botnets for distributing email and proxying user responses. In effect, the best way to measure spam is to be a spammer. In this paper, we have effectively conducted this study, though sidestepping the obvious legal and ethical problems associated with sending spam.c Critically, our study makes use of an existing spamming botnet. By infiltrating the botnet parasitically, we convinced it to modify a subset of the spam it already sends, thereby directing any interested recipients to Web sites under our control, rather than those belonging to the spammer. In turn, our Web sites presented "defanged" versions of the spammer's own sites, with functionality removed that would compromise the victim's system or receive sensitive personal information such as name, address or credit card information. Using this methodology, we have documented three spam campaigns comprising over 469 million emails. We identified how much of this spam is successfully delivered, how much is filtered by popular antispam solutions, and, most importantly, how many users "click-through" to the site being advertised (response rate) and how many of those progress to a "sale" or "infection" (conversion rate). The remainder of this paper is structured as follows. Section 2 describes the economic basis for spam and reviews prior research in this area. Section 4 describes our experimental methodology for botnet infiltration. Section 5 describes our spam filtering and conversion results, Section 6 analyzes the effects of blacklisting on spam delivery, and Section 7 analyzes the possible influences on spam responses. We synthesize our findings in Section 8 and conclude.