Matches in ScholarlyData for { ?s ?p ?o. }
- item-7 hasContent bin-wei.
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- 535 creator ryen-white.
- 535 creator steven-m-drucker.
- 535 type InProceedings.
- 535 label "Investigating Behavioral Variability in Web Search".
- 535 sameAs 535.
- 535 abstract "In this paper we describe a longitudinal log-based study that investigated variability in peoples' interaction behavior when engaged in search-related activities on the World Wide Web. We analyze the consistency of interaction patterns for more than two thousand volunteer users over a period of five months. Findings of our analysis suggest that there are dramatic differences in variability in key aspects of the interaction within and between queries, and within and between users. Our findings also indicate the existence of at least two distinct classes of user ' navigators and explorers ' who exhibit large differences in their search behavior. These findings have implications for the design of tools to support more effective search-related interactions on the Web.".
- 535 hasAuthorList authorList.
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- 535 title "Investigating Behavioral Variability in Web Search".
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- item-1 hasContent ryen-white.
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- item-2 hasContent steven-m-drucker.
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- 550 creator anthony-tomasic.
- 550 creator ian-fette.
- 550 creator norman-sadeh-koniecpol.
- 550 type InProceedings.
- 550 label "Learning to Detect Phishing Emails".
- 550 sameAs 550.
- 550 abstract "Each month, more attacks are launched with the aim of making web users believe that they are communicating with a trusted entity for the purpose of stealing account information, logon credentials, and identity information in general. This attack method, commonly known as "phishing," is most commonly initiated by sending out emails with links to spoofed websites that harvest information. We present a method for detecting these attacks, which in its most general form is an application of machine learning on a feature set designed to highlight user-targeted deception in electronic communication. This method is applicable, with slight modification, to detection of phishing websites, or the emails used to direct victims to these sites. We evaluate this method on a set of approximately 860 such phishing emails, and 6950 non-phishing emails, and correctly identify over 96% of the phishing emails while only mis-classifying on the order of 0.1% of the legitimate emails. We conclude with thoughts on the future for such techniques to specifically identify deception, specifically with respect to the evolutionary nature of the attacks and information available.".
- 550 hasAuthorList authorList.
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- 550 title "Learning to Detect Phishing Emails".
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- item-1 hasContent ian-fette.
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- item-2 hasContent norman-sadeh-koniecpol.
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- item-3 hasContent anthony-tomasic.
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- 551 creator eric-horvitz.
- 551 creator jure-leskovec.
- 551 creator susan-dumais.
- 551 type InProceedings.
- 551 label "Web Projections: Learning from Contextual Subgraphs of the Web".
- 551 sameAs 551.
- 551 abstract "Research on web search has demonstrated the value of using information about the graphical structure of the Web in ranking search results. To date, specific graphical properties have been used in these analyses. We introduce a {\em web projection} method that generalizes prior efforts of graphical relationships of the web in several ways. With the approach, we create subgraphs by projecting sets of pages and domains onto the larger web graph, and then use machine learning to construct predictive models that operate on graphical properties. We describe the method and then present experiments that illustrate the construction of predictive models of search result quality and user query reformulation.".
- 551 hasAuthorList authorList.
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- 551 title "Web Projections: Learning from Contextual Subgraphs of the Web".
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- item-1 hasContent jure-leskovec.
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- item-2 hasContent susan-dumais.
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- item-3 hasContent eric-horvitz.
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- 555 creator andrew-bortz.
- 555 creator dan-boneh.
- 555 type InProceedings.
- 555 label "Exposing Private Information by Timing Web Applications".
- 555 sameAs 555.
- 555 abstract "We show that the time web sites take to respond to HTTP requests can leak private information, using two different types of attacks. The first directly measures response times from a web site to expose private information such as validity of an username at a secured site or the number of private photos in a publicly viewable gallery. The second, called cross-site timing, enables a malicious web site to obtain information from the user's perspective at another site. For example, a malicious site can learn if the user is currently logged in at a victim site and, in some cases, the number of objects in the user's shopping cart. Our experiments suggest that these timing vulnerabilities are wide-spread. We explain in detail how and why these attacks work, and discuss methods for writing web application code that resists these attacks.".
- 555 hasAuthorList authorList.
- 555 isPartOf proceedings.
- 555 title "Exposing Private Information by Timing Web Applications".
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- item-1 hasContent andrew-bortz.
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- item-2 hasContent dan-boneh.
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- 557 creator jason-i-hong.
- 557 creator lorrie-cranor.
- 557 creator yue-zhang.
- 557 type InProceedings.
- 557 label "CANTINA: A Content-Based Approach to Detecting Phishing Web Sites".
- 557 sameAs 557.