Conscript Your Friends into Larger Anonymity Sets with JavaScript

We present the design and prototype implementation of ConScript, a framework for using JavaScript to allow casual Web users to participate in an anonymous communication system. When a Web user visits a cooperative Web site, the site serves a JavaScript application that instructs the browser to create and submit "dummy" messages into the anonymity system. Users who want to send non-dummy messages through the anonymity system use a browser plug-in to replace these dummy messages with real messages. Creating such conscripted anonymity sets can increase the anonymity set size available to users of remailer, e-voting, and verifiable shuffle-style anonymity systems. We outline ConScript's architecture, we address a number of potential attacks against ConScript, and we discuss the ethical issues related to deploying such a system. Our implementation results demonstrate the practicality of ConScript: a workstation running our ConScript prototype JavaScript client generates a dummy message for a mix-net in 81 milliseconds and it generates a dummy message for a DoS-resistant DC-net in 156 milliseconds.Comment: An abbreviated version of this paper will appear at the WPES 2013 worksho

XSS-FP: Browser Fingerprinting using HTML Parser Quirks

There are many scenarios in which inferring the type of a client browser is desirable, for instance to fight against session stealing. This is known as browser fingerprinting. This paper presents and evaluates a novel fingerprinting technique to determine the exact nature (browser type and version, eg Firefox 15) of a web-browser, exploiting HTML parser quirks exercised through XSS. Our experiments show that the exact version of a web browser can be determined with 71% of accuracy, and that only 6 tests are sufficient to quickly determine the exact family a web browser belongs to

State College Times, October 13, 1933

Volume 22, Issue 14https://scholarworks.sjsu.edu/spartandaily/12908/thumbnail.jp

Unsupervised Anomaly-based Malware Detection using Hardware Features

Recent works have shown promise in using microarchitectural execution patterns to detect malware programs. These detectors belong to a class of detectors known as signature-based detectors as they catch malware by comparing a program's execution pattern (signature) to execution patterns of known malware programs. In this work, we propose a new class of detectors - anomaly-based hardware malware detectors - that do not require signatures for malware detection, and thus can catch a wider range of malware including potentially novel ones. We use unsupervised machine learning to build profiles of normal program execution based on data from performance counters, and use these profiles to detect significant deviations in program behavior that occur as a result of malware exploitation. We show that real-world exploitation of popular programs such as IE and Adobe PDF Reader on a Windows/x86 platform can be detected with nearly perfect certainty. We also examine the limits and challenges in implementing this approach in face of a sophisticated adversary attempting to evade anomaly-based detection. The proposed detector is complementary to previously proposed signature-based detectors and can be used together to improve security.Comment: 1 page, Latex; added description for feature selection in Section 4, results unchange