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The Heartbleed vulnerability took the Internet by surprise in April 2014. The vulnerability, one of the most consequential since the advent of the commercial Internet, allowed attackers to remotely read protected memory from an estimated 24--55% of popular HTTPS sites. In this work, we perform a comprehensive, measurement-based analysis of the(More)
In this study we expose the serious large-scale threat of criminal account hijacking and the resulting damage incurred by users and web services. We develop a system for detecting large-scale attacks on Twitter that identifies 14 million victims of compromise. We examine these accounts to track how attacks spread within social networks and to determine how(More)
Many distributed systems are subject to the <i>Sybil</i> attack, where an adversary subverts system operation by emulating the behavior of multiple distinct nodes. Most recent works addressing this problem leverage social networks to establish trust relationships between users. However, social networks are not appropriate in all systems. They can be(More)
Security researchers can send vulnerability notifications to take proactive measures in securing systems at scale. However, the factors affecting a notification’s efficacy have not been deeply explored. In this paper, we report on an extensive study of notifying thousands of parties of security issues present within their networks, with an aim of(More)
Rich client-side applications written in HTML5 proliferate on diverse platforms, access sensitive data, and need to maintain dataconfinement invariants. Applications currently enforce these invariants using implicit, ad-hoc mechanisms. We propose a new primitive called a data-confined sandbox or DCS. A DCS enables complete mediation of communication(More)
Laser transmission spectroscopy (LTS) is a quantitative and rapid in vitro technique for measuring the size, shape, and number of nanoparticles in suspension. Here we report on the application of LTS as a novel detection method for species-specific DNA where the presence of one invasive species was differentiated from a closely related invasive sister(More)
The k-nearest neighbors (k-NN) algorithm is a popular and effective classification algorithm. Due to its large storage and computational requirements, it is suitable for cloud outsourcing. However, k-NN is often run on sensitive data such as medical records, user images, or personal information. It is important to protect the privacy of data in an(More)
By examining the impact of an increase in correlation among underlying assets on the value of a collateralized debt obligation (CDO) equity tranche, we show that, contrary to general perception, CDO equity can be short on correlation. Specifically, when the underlying reference portfolio comprises high quality assets (assets with low probability of default)(More)
As miscreants routinely hijack thousands of vulnerable web servers weekly for cheap hosting and traffic acquisition, security services have turned to notifications both to alert webmasters of ongoing incidents as well as to expedite recovery. In this work we present the first large-scale measurement study on the effectiveness of combinations of browser,(More)
We describe the implementation of precision laser transmission spectroscopy for sizing and counting nanoparticles in suspension. Our apparatus incorporates a tunable laser and balanced optical system that measures light transmission over a wide (210-2300 nm) wavelength range with high precision and sensitivity. Spectral inversion is employed to determine(More)