The com, net, and org TLDs contain roughly 150 million registered domains, and domain registrants often have a difficult time finding a desirable and available name. In 2013, ICANN began delegation of a new wave of TLDs into the Domain Name System with the goal of improving meaningful name choice for registrants. The new rollout resulted in over 500 new TLDs in the first 18 months, nearly tripling the number of TLDs. Previous rollouts of small numbers of new TLDs have resulted in a burst of defensive registrations as companies aggressively defend their trademarks to avoid consumer confusion. This paper analyzes the types of domain registrations in the new TLDs to determine registrant behavior in the brave new world of naming abundance. We also examine the cost structures and monetization models for the new TLDs to identify which registries are profitable. We gather DNS, Web, and WHOIS data for each new domain, and combine this with cost structure data from ICANN, the registries, and domain registrars to estimate the total cost ofthe new TLD program. We find that only 15% of domains in the new TLDs show characteristics consistent with primary registrations, while the rest are promotional, speculative, or defensive in nature; indeed, 16% of domains with NS records do not even resolve yet, and 32% are parked. Our financial analysis suggests only half of the registries have earned enough to cover their application fees, and 10% of current registries likely never will solely from registration revenue.

We investigate a Gaussian latent variable model for semi-supervised learning of linear large margin classifiers. The model’s latent variables encode the signed distance of examples to the separating hyperplane, and we constrain these variables, for both labeled and unlabeled examples, to ensure that the classes are separated by a large margin. Our approach is based on similar intuitions as semi-supervised support vector machines (S3VMs), but these intuitions are formalized in a probabilistic framework. Within this framework we are able to derive an especially simple Expectation-Maximization (EM) algorithm for learning. The algorithm alternates between applying Bayes rule to “fill in” the latent variables (the E-step) and performing an unconstrained least-squares regression to update the weight vector (the M-step). For the best results it is necessary to constrain the unlabeled data to have a similar ratio of positive to negative examples as the labeled data. Within our model this constraint renders exact inference intractable, but we show that a Lyapunov central limit theorem (for sums of independent, but non-identical random variables) provides an excellent approximation to the true posterior distribution. We perform experiments on large-scale text classification and find that our model significantly outperforms existing implementations of S3VMs.

Black hat search engine optimization (SEO), the practice of abusively manipulating search results, is an enticing method to acquire targeted user traffic. In turn, a range of interventions--from modifying search results to seizing domains--are used to combat this activity. In this paper, we examine the effectiveness of these interventions in the context of an understudied market niche, counterfeit luxury goods. Using eight months of empirical crawled data, we identify 52 distinct SEO campaigns, document how well they are able to place search results for sixteen luxury brands, how this capability impacts the dynamics of their order volumes and how well existing interventions undermine this business when employed.

We describe an automated system for the large-scale monitoring of Web sites that serve as online storefronts for spam-advertised goods. Our system is developed from an extensive crawl of black-market Web sites that deal in illegal pharmaceuticals, replica luxury goods, and counterfeit software. The operational goal of the system is to identify the affiliate programs of online merchants behind these Web sites; the system itself is part of a larger effort to improve the tracking and targeting of these affiliate programs. There are two main challenges in this domain. The first is that appearances can be deceiving: Web pages that render very differently are often linked to the same affiliate program of merchants. The second is the difficulty of acquiring training data: the manual labeling of Web pages, though necessary to some degree, is a laborious and time-consuming process. Our approach in this paper is to extract features that reveal when Web pages linked to the same affiliate program share a similar underlying structure. Using these features, which are mined from a small initial seed of labeled data, we are able to profile the Web sites of forty-four distinct affiliate programs that account, collectively, for hundreds of millions of dollars in illicit e-commerce. Our work also highlights several broad challenges that arise in the large-scale, empirical study of malicious activity on the Web.

We describe a latent variable model for supervised dimensionality reduction and distance metric learning. The model discovers linear projections of high dimensional data that shrink the distance between similarly labeled inputs and expand the distance between differently labeled ones. The model’s continuous latent variables locate pairs of examples in a latent space of lower dimensionality. The model differs significantly from classical factor analysis in that the posterior distribution over these latent variables is not always multivariate Gaussian. Nevertheless we show that inference is completely tractable and derive an Expectation-Maximization (EM) algorithm for parameter estimation. We also compare the model to other approaches in distance metric learning. The model’s main advantage is its simplicity: at each iteration of the EM algorithm, the distance metric is re-estimated by solving an unconstrained least-squares problem. Experiments show that these simple updates are highly effective.