Malware spread among websites and between websites and clients is an increasing problem. Search engines play an important role in directing users to websites and are a natural control point for intervening, using mechanisms such as blacklisting. The paper presents a simple Markov model of malware spread through large populations of websites and studies the effect of two interventions that might be deployed by a search provider: blacklisting infected web pages by removing them from search results entirely and a generalization of blacklisting, called depreferencing, in which a website's ranking is decreased by a fixed percentage each time period the site remains infected. We analyze and study the trade-offs between infection exposure and traffic loss due to false positives (the cost to a website that is incorrectly blacklisted) for different interventions. As expected, we find that interventions are most effective when websites are slow to remove infections. Surprisingly, we also find that low infection or recovery rates can increase traffic loss due to false positives. Our analysis also shows that heavy-tailed distributions of website popularity, as documented in many studies, leads to high sample variance of all measured outcomes. These result implies that it will be difficult to determine empirically whether certain website interventions are effective, and it suggests that theoretical models such as the one described in this paper have an important role to play in improving web security.
An emerging consensus among policy makers is that interventions undertaken by Internet Service Providers are the best way to counter the rising incidence of malware. However, assessing the suitability of countermeasures at this scale is hard. In this paper, we use an agent-based model, called ASIM, to investigate the impact of policy interventions at the Autonomous System level of the Internet. For instance, we find that coordinated intervention by the 0.2 -biggest ASes is more effective than uncoordinated efforts adopted by 30 of all ASes. Furthermore, countermeasures that block malicious transit traffic appear more effective than ones that block outgoing traffic. The model allows us to quantify and compare positive externalities created by different countermeasures. Our results give an initial indication of the types and levels of intervention that are most cost-effective at large scale. 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 -- usingextensive 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.  Online service providers are engaged in constant conflict with miscreants who try to siphon a portion of legitimate traffic to make illicit profits. We study the abuse of "trending" search terms, in which miscreants place links to malware-distributing or ad-filled web sites in web search and Twitter results, by collecting and analyzing measurements over nine months from multiple sources. We devise heuristics to identify ad-filled sites, report on the prevalence of malware and ad-filled sites in trending-term search results, and measure the success in blocking such content. We uncover collusion across offending domains using network analysis, and use regression analysis to conclude that both malware and ad-filled sites thrive on less popular, and less profitable trending terms. We build an economic model informed by our measurements and conclude that ad-filled sites and malware distribution may be economic substitutes. Finally, because our measurement interval spans February 2011, when Google announced changes to its ranking algorithm to root out low-quality sites, we can assess the impact of search-engine intervention on the profits miscreants can achieve.