Minimal adversarial perturbations added to inputs have been shown to be effective at fooling deep neural networks. In this paper, we introduce several innovations that make white-box targeted attacks follow the intuition of the attacker's goal: to …
Motivated by the transformative impact of deep neural networks (DNNs) in various domains, researchers and anti-virus vendors have proposed DNNs for malware detection from raw bytes that do not require manual feature engineering. In this work, we …
Smart-home devices are becoming increasingly ubiquitous and interconnected with other devices and services, such as phones, fitness trackers, cars, and social media accounts. Built-in connections between these services are still emerging, but …
Understanding users' perceptions of suspected computer-security problems can help us tailor technology to better protect users. To this end, we conducted a field study of users' perceptions using 189,272 problem descriptions sent to the …
Many computer-security defenses are reactive---they operate only when security incidents take place, or immediately thereafter. Recent efforts have attempted to predict security incidents before they occur, to enable defenders to proactively protect …
Cross-site scripting (XSS) vulnerabilities are the most frequently reported web application vulnerability. As complex JavaScript applications become more widespread, DOM (Document Object Model) XSS vulnerabilities---a type of XSS vulnerability where …
Studies of Internet censorship rely on an experimental technique called probing. From a client within each country under investigation, the experimenter attempts to access network resources that are suspected to be censored, and records what happens. …
Computer security tools usually provide universal solutions without taking user characteristics (origin, income level, ...) into account. In this paper, we test the validity of using such universal security defenses, with a particular focus on …
Machine learning is enabling a myriad innovations, including new algorithms for cancer diagnosis and self-driving cars. The broad use of machine learning makes it important to understand the extent to which machine-learning algorithms are subject to …
Online trackers compile profiles on users for targeting ads, customizing websites, and selling users' information. In this paper, we report on the first detailed study of the perceived benefits and risks of tracking---and the reasons behind …