2098964
Spam double-funnel: connecting web spammers with advertisers
Spammers use questionable search engine optimization (SEO) techniques to promote their spam links into top search results. In this paper, we focus on one prevalent type of spam - redirection spam - where one can identify spam pages by the third-party domains that these pages redirect traffic to. We propose a five-layer, double-funnel model for describing end-to-end redirection spam, present a methodology for analyzing the layers, and identify prominent domains on each layer using two sets of commercial keywords. one targeting spammers and the other targeting advertisers. The methodology and findings are useful for search engines to strengthen their ranking algorithms against spam, for legitimate website owners to locate and remove spam doorway pages, and for legitimate advertisers to identify unscrupulous syndicators who serve ads on spam pages.
9276534
Setuid Demystified
1194024
Hierarchical classification of Web content
This paper explores the use of hierarchical structure for classifying a large, heterogeneous collection of web content. The hierarchical structure is initially used to train different second-level classifiers. In the hierarchical case, a model is learned to distinguish a second-level category from other categories within the same top level. In the flat non-hierarchical case, a model distinguishes a second-level category from all other second-level categories. Scoring rules can further take advantage of the hierarchy by considering only second-level categories that exceed a threshold at the top level.
We use support vector machine (SVM) classifiers, which have been shown to be efficient and effective for classification, but not previously explored in the context of hierarchical classification. We found small advantages in accuracy for hierarchical models over flat models. For the hierarchical approach, we found the same accuracy using a sequential Boolean decision rule and a multiplicative decision rule. Since the sequential approach is much more efficient, requiring only 14%-16% of the comparisons used in the other approaches, we find it to be a good choice for classifying text into large hierarchical structures.
1402963
MOPS: an infrastructure for examining security properties of software
We describe a formal approach for finding bugs in security-relevant software and verifying their absence. The idea is as follows: we identify rules of safe programming practice, encode them as safety properties, and verify whether these properties are obeyed. Because manual verification is too expensive, we have built a program analysis tool to automate this process. Our program analysis models the program to be verified as a pushdown automaton, represents the security property as a finite state automaton, and uses model checking techniques to identify whether any state violating the desired security goal is reachable in the program. The major advantages of this approach are that it is sound in verifying the absence of certain classes of vulnerabilities, that it is fully interprocedural, and that it is efficient and scalable. Experience suggests that this approach will be useful in finding a wide range of security vulnerabilities in large programs efficiently.
14045822
Optimizing search by showing results in context
We developed and evaluated seven interfaces for integrating semantic category information with Web search results. List interfaces were based on the familiar ranked-listing of search results, sometimes augmented with a category name for each result. Category interfaces also showed page titles and/or category names, but re-organized the search results so that items in the same category were grouped together visually. Our user studies show that all Category interfaces were more effective than List interfaces even when lists were augmented with category names for each result. The best category performance was obtained when both category names and individual page titles were presented. Either alone is better than a list presentation, but both together provide the most effective means for allowing users to quickly examining search results. These results provide a better understanding of the perceptual and cognitive factors underlying the advantage of category groupings and provide some practical guidance to Web search interface designers.
1567676
Bringing order to the Web: automatically categorizing search results
We developed a user interface that organizes Web search results into hierarchical categories. Text classification algorithms were used to automatically classify arbitrary search results into an existing category structure on-the-fly. A user study compared our new category interface with the typical ranked list interface of search results. The study showed that the category interface is superior both in objective and subjective measures. Subjects liked the category interface much better than the list interface, and they were 50% faster at finding information that was organized into categories. Organizing search results allows users to focus on items in categories of interest rather than having to browse through all the results sequentially.
195351528
Predicting Internet end-to-end delay: a multiple-model approach
6650752
Using Content-Based and Link-Based Analysis in Building Vertical Search Engines
This paper reports our research in the Web page filtering process in specialized search engine development. We propose a machine-learning-based approach that combines Web content analysis and Web structure analysis. Instead of a bag of words, each Web page is represented by a set of content-based and link-based features, which can be used as the input for various machine learning algorithms. The proposed approach was implemented using both a feedforward/backpropagation neural network and a support vector machine. An evaluation study was conducted and showed that the proposed approaches performed better than the benchmark approaches.
6295939
Dental Biometrics: Human Identification Using Dental Radiographs
The goal of forensic dentistry is to identify people based on their dental records, mainly as radiograph images. In this paper we attempt to set forth the foundations of a biometric system for semi-automatic processing and matching of dental images, with the final goal of human identification. Given a dental record, usually as a postmortem (PM) radiograph, we need to search the database of antemortem (AM) radiographs to determine the identity of the person associated with the PM image. We use a semi-automatic method to extract shapes of the teeth from the AM and PM radiographs, and find the affine transform that best fits the shapes in the PM image to those in the AM images. A ranking of matching scores is generated based on the distance between the AM and PM tooth shapes. Initial experimental results on a small database of radiographs indicate that matching dental images based on tooth shapes and their relative positions is a feasible method for human identification.
8488192
Session-based overload control in QoS-aware Web servers
With the explosive use of the Internet, contemporary Web servers are susceptible to overloads and their services deteriorate drastically and often cause denial of services. In this paper, we propose two methods to prevent and control overloads in Web servers by utilizing the session-based relationship among HTTP requests. We first exploited the dependence among session-based requests by analyzing and predicting the reference patterns. Using the dependency relationships, we have derived traffic conformation functions that can be used for capacity planning and overload prevention in Web servers. Second, we have proposed a dynamic weighted fair sharing (DWFS) scheduling algorithm to control overloads in Web servers. DWFS is distinguished from other scheduling algorithms in the sense that it aims to avoid processing of requests that belong to sessions that are likely to be aborted in the near future. The experimental results demonstrate that DWFS can improve server responsiveness by as high as 50% while providing QoS support through service differentiation for a class of application environment.
5239541
Link prediction approach to collaborative filtering
Recommender systems can provide valuable services in a digital library environment, as demonstrated by its commercial success in book, movie, and music industries. One of the most commonly-used and successful recommendation algorithms is collaborative filtering, which explores the correlations within user-item interactions to infer user interests and preferences. However, the recommendation quality of collaborative filtering approaches is greatly limited by the data sparsity problem. To alleviate this problem we have previously proposed graph-based algorithms to explore transitive user-item associations. In this paper, we extend the idea of analyzing user-item interactions as graphs and employ link prediction approaches proposed in the recent network modeling literature for making collaborative filtering recommendations. We have adapted a wide range of linkage measures for making recommendations. Our preliminary experimental results based on a book recommendation dataset show that some of these measures achieved significantly better performance than standard collaborative filtering algorithms